Jeffries-Matusita Distance Research Articles

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification-Decoding Lateral Epicondylitis in Forearm Muscles.

Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase-amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject's label prediction. Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries-Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. This study's implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders.

Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt

Machine learning and remote sensing techniques are widely accepted as valuable, cost-effective tools in lithological discrimination and mineralogical investigations. The current study represents an attempt to use machine learning classification along with several remote sensing techniques being applied to Landsat-8/9 satellite data to discriminate the various outcropping lithological rock units at the Duwi Shear Belt (DSB) area in the Central Eastern Desert of Egypt. Multi-class machine learning classification, multiple conventional remote sensing mapping techniques, spectral separability analysis based on the Jeffries-Matusita (J-M) distance measure, fieldwork, and petrographic investigations were integrated to enhance the lithological discrimination of the exposed rock units at DSB area. The well-recognized machine learning classifier (Support Vector Machine—SVM) was adopted in this study, with training data determined carefully based on enhancing the lithological discrimination attained from various remote sensing techniques of False Color Composites (FCC), Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF), along with the fieldwork data and the previously published geologic maps. High overall accuracy of the SVM classification was obtained, however, inspection of the individual rock unit classes’ accuracies revealed lower accuracy for certain types of rock units which were also found associated with lower separability scores as well. Among the least separable rock units were; metagabbro rocks that showed high spectral similarity with the volcaniclastic metasediments rocks, and the metaultramafics of the ophiolitic mélange showed spectral attitude of high correlation to that of the Hammamat volcanosedimentary rocks. Target-oriented Color Ratio Composites (CRC) technique was implemented to better discriminate these hardly separable rock units. A final integrated geological map was obtained comprising the various discriminated Neoproterozoic basement rock units of the DSB area. The successfully mapped litho-units include; Meatiq Group (amphibolites, gneissic granitoids, and mylonitized granitoids), ophiolitic mélange (metaultramafics, metagabbro-amphibolites, and volcaniclastic metasediments), Dokhan volcanics, Hammamat sediments, and granites. An adequate description of these rock units was also given in light of the conducted intense fieldwork and petrographic investigations.

Index-based forest degradation mapping using high and medium resolution multispectral sensors

ABSTRACT Monitoring dense forest ecosystems, such as the laurel forest in Garajonay National Park, is vital for biodiversity conservation, carbon storage, and ecological balance. This study employs satellite remote sensing technologies to introduce a novel methodology, based on vegetation indices, aiming to assess and protect the health of the forest. Utilizing the Jeffries-Matusita distance and a histogram-based method, optimal indices to map forest degradation, like Wide Dynamic Range Vegetation Index (WDRVI) and Modified Simple Ratio (MSR), were identified among 19 generated indices. The study processed imagery from three satellite sensors (WorldView-2, PlanetScope and Sentinel-2), producing maps distinguishing healthy and degraded areas. The study’s practical significance lies in offering a method to assess the suitability of sensors and indices for effectively mapping forest degradation. This approach aids conservation efforts and provides valuable insights for environmental managers and policymakers, facilitating the implementation of targeted strategies to safeguard Garajonay National Park's unique laurel forest ecosystem. Emphasizing the role of remote sensing in practical vegetation protection endeavors, the study contributes to on-the-ground initiatives, ensuring the preservation and sustainability of the park's rich biodiversity.

Using Improved YOLOv5 and SegFormer to Extract Tailings Ponds from Multi-Source Data

This paper proposes a framework that combines the improved "You Only Look Once" version 5 (YOLOv5) and SegFormer to extract tailings ponds from multi-source data. Points of interest (POIs) are crawled to capture potential tailings pond regions. Jeffries–Matusita distance is used to evaluate the optimal band combination. The improved YOLOv5 replaces the backbone with the PoolFormer to form a PoolFormer backbone. The neck introduces the CARAFE operator to form a CARAFE feature pyramid network neck (CRF-FPN). The head is substituted with an efficiency decoupled head. POIs and classification data optimize improved YOLOv5 results. After that, the SegFormer is used to delineate the boundaries of tailings ponds. Experimental results demonstrate that the mean average precision of the improved YOLOv5s has increased by 2.78% compared to the YOLOv5s, achieving 91.18%. The SegFormer achieves an intersection over union of 88.76% and an accuracy of 94.28%.

Artificial afforestation increased the distribution area and landscape complexity of planted forests in Tibet over the past 30 years

Afforestation plays an important role in improving the ecological environment and promoting local economic development in countries around the world. Although the artificial afforestation project in Tibet has been conducted for more than 30 years since in 1990 s, few studies have examined the dynamic changes in landscape patterns of planted forests in this region since the implementation of the project. This study used Google Earth Engine (GEE) and Landsat data from 1990, 2000, 2010 and 2020 to construct a Normalized Difference Vegetation Index (NDVI) time series curves and an optimal classification model based on the Jeffries-Matusita (J-M) distance. Then, the changes in landscape indices of planted forests in the Tibet Autonomous Region over the past 30 years were examined. The results showed that the area of planted forests increased from 9889.20 ha to 165,879.36 ha by artificial planting, with an average annualized growth rate of about 9.86%. In terms of the landscape pattern, the connectivity and aggregation degree of planted forests increased; the Cohesion (COH) and Aggregation Index (AI) increased by 44.60% and 26.03%, respectively. However, the fragmentation indices also increased to a certain extent due to the increased area and discontinuous planting; the Number of Patches (NP), Patch Density (PD) and Landscape Shape Index (LSI) increased by 32,262, 0.03 and 138.88, respectively. Among the seven regions of Tibet, these changes were most obvious in Lhasa, Qamdo and Shannan regions. Our results indicate that the planted forest area has significantly increased over the past 30 years although the landscape complexity has also increased because of discontinuous planting. These findings highlight the remarkable achievements of the Tibetan afforestation project over the past 30 years.

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

Generating orchards spatial distribution maps within a heterogeneous landscape is challenging and requires fine spatial and temporal resolution images. This study examines the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) satellite data of relatively high spatial and temporal resolutions for discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-temporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 and S2 (scheme-6) outperforming all other combination schemes. The spectral separability between orchards pairs was assessed using Jeffries-Matusita (JM) distance, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. The performance difference between RF and SVM was not significant, SVM showed a slightly higher accuracy, except for scheme-4 where RF performed better. This study concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification. Prospectively, these findings will be helpful for orchard monitoring, improvement of yield estimation and precision based agricultural practices.

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACT Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

Marine oil spill detection using improved polarimetric feature based on polarization SAR image

ABSTRACT Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications.

Mapping cropland in Yunnan Province during 1990–2020 using multi-source remote sensing data with the Google Earth Engine Platform

Cropland is the foundation of agriculture and the cornerstone of ensuring national food security. Ongoing advancement of China’s urbanization and social economy results in a tendency toward ‘non-agriculturalization’ of cropland in pursuit of economic benefits, which greatly threatens food security. Therefore, long-term monitoring of cropland is urgently needed. Based on the Google Earth Engine (GEE) platform, this paper uses the Landsat series and Sentinel-2 multisource images from 1990–2020 to dynamically monitor cropland in Yunnan Province. First, a multiresolution collaborative sample selection method was proposed and sample labels with resolutions better than 30 m from 1990 to 2015 were obtained. An ‘impervious surface-spectrum-texture-geometry’ (ISTG) feature system was constructed and the Jeffries-Matusita (JM) distance method was used for feature optimization. Finally, the proposed collaborative classification system method of land classification and binary classification was used to extract cropland in Yunnan Province. The results show that: (1) The accuracy of cropland extraction was highly reliable, with an overall accuracy (OA) wall-to-wall ranging from 93.15–98.14% and a kappa coefficient (Kappa) ranging from 86.29–96.26%. (2) The change trend of the cropland area in Yunnan Province from 1990 to 2020 exhibited certain fluctuations, but overall decreased annually. (3) The province’s average annual cropland area is 567.71 × 103 hectares. The proposed method has great potential in large-scale cropland extraction.

Mitigating the influence of topographies on hyperspectral images for the sorting of iron ore based on variational mode decomposition and spectral property

Hyperspectral imaging (HSI) can be applied for the purpose of sorting iron ores with varying total iron (TFe) content. However, the surface topographies of iron ore can negatively impact its hyperspectral images, consequently affecting the sorting effectiveness for each individual piece of iron ore. To address this issue, this study conducted an experiment on hyperspectral image of the iron ores. Firstly, variational mode decomposition (VMD) was employed to decompose the spectra of iron ore into trend signals and peak-valley signals. The two signals were then used for the calculation of three spectral properties (baseline-shift level, steepness level, and amplitude level). To identify the critical property capable of distinguishing normal spectra and the topographies-affected, the difference of the three spectral properties between the topographies-affected and the normal spectra were analyzed. According to the analysis results, a VMD-based correction model (VMDC) was proposed, which aims to identify the topographies-affected spectra in an ore and then correct them to resemble the normal spectra. Finally, the coefficient of variation (CV) for the correction was assessed and a test was conducted for TFe content prediction and sorting of each piece of iron ore. There are three primary conclusions summarized as follows:1. The mean Jeffries–Matusita distance (JMD) of baseline-shift level, steepness level, and amplitude level between normal spectra and topographies-affected spectra were 1.889, 1.307, and 1.170, respectively. This implies that baseline-shift level can be regarded as a key property for distinguishing whether a spectrum is topographies-affected or not. 2. After correction, there was a noticeable improvement in the topographies-affected areas of the ore samples, and the topographies-affected spectra showed the consistence with the normal spectra. Upon examination, the mean CV for VMDC is 0.115, whereas the uncorrected data has a mean CV of 0.315, indicating a reduction in spectral variability after VMDC correction. The results demonstrate the effectiveness of VMDC in mitigating the impact of topographies. 3. In the TFe content prediction and sorting, VMDC consistently demonstrates the best performance, for example when using RF as the regressor, coefficient of determination (R2) and accuracy of Original Data-RF, MSC-RF and VMDC-RF were 84.3% and 0.894, 86.7% and 0.903, and 88.7% and 0.921. The evaluation results confirm that VMDC can improve iron ore sorting. Overall, the proposed method shows potential for practical applications in iron ore sorting.

Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

Novel oil spill indices for sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait

Oil spills are a paramount and immediate challenge affecting marine ecosystems globally. Effective and timely monitoring tools, such as oil detection indices, offer a swift means to track oil spill spread across vast oceanic expanses. Moreover, these indices enhance data clarity, making it more conducive for machine learning and deep learning algorithms.This study leverages the natural seepage occurring around Qaruh Island, Kuwait as a unique context for the spectral analysis of oil spills using Sentinel-2 multispectral imagery due to repeated occurrences in the same region. This research evaluated 859 single band and 455 multichannel combinations to identify the most effective combinations in oil-water separability, employing the Jeffries-Matusita (JM) distance measure as a key metric. Bands 1, 2, 3, 8A, 11, and 12 consistently featured among the top-performing indices combinations B1−B11B1+B11;B1+B2B3+B11;B1+B2B3+B12;B1+B2B3+B8A affirming the significant effect of oil spills on visible, Near-Infrared (NIR), and Shortwave Infrared (SWIR) bands. Notably, the indices developed in this study outperformed those from prior research in terms of suitability to unsupervised classification algorithms. A significant conclusion of this study is that incorporating a higher number of bands in the analysis did not correlate with an increase in JM values, suggesting that the selection of specific, informative bands is more critical than the volume of input data. These findings underscore the indispensable role of Sentinel-2 imagery in environmental investigations and highlight the potential for focused, efficient analysis using strategic band combinations for effective oil spill detection.•This study identified optimized Sentinel-2 band combinations for oil-water separability, benefiting from naturally occurring spills around Qaruh Island.•The proposed indices outperformed the previous indices for oil spill visualization and clustering.•The new indices highlighted the critical role of specific band selection over the volume of input data for effective oil spill detection.

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

Development of a new index for mapping urban areas in Türkiye using Sentinel-2 images

Population growth and urbanization have been increasing simultaneously and rapidly in recent years. Urban growth causes changes in land use/cover. Monitoring and mapping these changes are important for issues such as disaster management, biodiversity and city and environmental planning and management. Images obtained by remote sensing methods provide data for mapping urban land use areas. Over the years, many approaches have been developed to accurately analyse these images. However, urban area mapping is still a problem due to its complex structure, the many different materials used in urban areas, artificial and natural green areas, roads and industrial zones. In this study, a new approach is proposed to accurately map urban areas. Band values of Sentinel-2 images are analysed in regions (Eskişehir, İnegöl area from Bursa and Sincan area from Ankara) belonging to various land classes in urban areas. Based on the results, a new index (Urban Area Index) was proposed. The performance of the index was evaluated by separability analyses (Bhattacharyya-distance, Jeffries-Matusita distance and Mahalanobis-distance). Besides the created index was classified with the Random Forests algorithm and an accuracy assessment was performed with the overall accuracy, producers’ and users’ accuracy and F1 score. Results obtained by classification using this index are compared to 17 indexes presented in the literature. The proposed index achieved an overall accuracy rate of over 82% for all study areas. This rate is higher than the overall accuracies obtained with other indices and raw Sentinel-2 bands. This result indicates that the proposed method successfully maps urban areas more effectively than existing methods.

Automatic Extraction of the Spatial Distribution of Picea schrenkiana in the Tianshan Mountains Based on Google Earth Engine and the Jeffries–Matusita Distance

As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 105 km2) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain.

Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China

China is the world's largest tea producer and trader. From 1995 to 2020, the plantation area and tea production increased by approximately 200 and 400%, respectively. Therefore, we must accurately identify the distribution of tea plantations to optimize production and promote sustainable development. However, most studies on the identification of tea plantations rely on local hyperspectral data processing, which have limitations related to a small scale, significant time consumption, and difficulty in integrating multisource images and multidimensional features to achieve the accuracy and optimization of the results. Based on the Google Earth Engine (GEE) platform, we integrated the spectral, synthetic aperture radar (SAR), terrain, and textural features to extract the seasonal phenological index using specific phenological information on tea plantations. Then, the random forest (RF) algorithm was applied to evaluate the importance of each feature in the initial feature set, followed by screening of the optimal feature combination and verification of the Jeffries–Matusita (JM) distance. Finally, the optimal feature combination was used as the input variable for the RF classification algorithm to identify the distribution of tea plantations in Hangzhou, China. The following outcomes were achieved: (1) compared to the processing of local hyperspectral data, the identification of tea plantations using the GEE had notable advantages, such as high automation, convenient algorithm adjustment, and rapid iteration of the results. (2) The user and producer accuracies of the classification of tea plantations using SAR and terrain and textural features improved from 89.3 and 85.9% to 95.8 and 91.7%, respectively, compared to only using spectral information. (3) The high-contribution feature combination identified by the importance analysis using the RF algorithm increased the JM distance to a high degree of separability of approximately 2, which is highly effective for the optimization of classification features. (4) In addition to the visible and near-infrared bands commonly used in spectral information, short-wave infrared and vegetation red-edge bands can play important roles in identifying tea plantations. (5) The importance of the same spectral or SAR information varied for different seasonal phenological indicators, indicating that the addition of phenological information was beneficial for the identification of tea plantations.

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Accurate land cover classification (LCC) is essential for studying global change. Synthetic aperture radar (SAR) has been used for LCC due to its advantage of weather independence. In particular, the dual-polarization (dual-pol) SAR data have a wider coverage and are easier to obtain, which provides an unprecedented opportunity for LCC. However, the dual-pol SAR data have a weak discrimination ability due to limited polarization information. Moreover, the complex imaging mechanism leads to the speckle noise of SAR images, which also decreases the accuracy of SAR LCC. To address the above issues, an improved dual-pol radar vegetation index based on multiple components (DpRVIm) and a new LCC method are proposed for dual-pol SAR data. Firstly, in the DpRVIm, the scattering information of polarization and terrain factors were considered to improve the separability of ground objects for dual-pol data. Then, the Jeffries-Matusita (J-M) distance and one-dimensional convolutional neural network (1DCNN) algorithm were used to analyze the effect of difference dual-pol radar vegetation indexes on LCC. Finally, in order to reduce the influence of the speckle noise, a two-stage LCC method, the 1DCNN-MRF, based on the 1DCNN and Markov random field (MRF) was designed considering the spatial information of ground objects. In this study, the HH-HV model data of the Gaofen-3 satellite in the Dongting Lake area were used, and the results showed that: (1) Through the combination of the backscatter coefficient and dual-pol radar vegetation indexes based on the polarization decomposition technique, the accuracy of LCC can be improved compared with the single backscatter coefficient. (2) The DpRVIm was more conducive to improving the accuracy of LCC than the classic dual-pol radar vegetation index (DpRVI) and radar vegetation index (RVI), especially for farmland and forest. (3) Compared with the classic machine learning methods K-nearest neighbor (KNN), random forest (RF), and the 1DCNN, the designed 1DCNN-MRF achieved the highest accuracy, with an overall accuracy (OA) score of 81.76% and a Kappa coefficient (Kappa) score of 0.74. This study indicated the application potential of the polarization decomposition technique and DEM in enhancing the separability of different land cover types in SAR LCC. Furthermore, it demonstrated that the combination of deep learning networks and MRF is suitable to suppress the influence of speckle noise.

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

Automated mucilage extraction index (AMEI): a novel spectral water index for identifying marine mucilage formations from Sentinel-2 imagery

ABSTRACT Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and marine pollution. Monitoring sea surface mucilage formations and mapping their spatial distributions provide valuable information to the local authorities and decision-makers in developing prevention and rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) that allows effective and accurate detection of surface mucilage aggregates using Sentinel-2 satellite imagery. The index uses four bands of Sentinel-2 Level-2A imagery (Bands 3, 4, 8, and 12) covering visible, near-infrared, and shortwave infrared regions. The index was formulated considering the image acquired on 19 May 2021, when mucilage formations were most intensively observed in the Sea of Marmara. The performance of the developed index was then evaluated using the images acquired on 14 and 24 May and 13 June 2021. The Jenks Natural Breaks (JNB) algorithm was applied to estimate the threshold values for separating mucilage formations from the water surface background and classify index maps into two classes: ‘mucilage’ and ‘others’. To statistically analyse the effectiveness of the indices in distinguishing water background and mucilage formations, the proposed index was compared with 21 widely used water indices by applying Bhattacharyya Distance, Jeffries-Matusita Distance, and M-Statistic measures. Results confirm the robustness of the proposed spectral index, offering superior separation performance (above 1.5 in terms of M-Statistic) compared to other water indices on both cloud-free images and images with cumulus clouds. Visual interpretations also verified that boundaries of the mucilage formations in the cloudless and thin cloudy images were accurately identified by the proposed index, and different types of mucilage (i.e. yellow and white) can be identified when an appropriate histogram thresholding is applied.

Spatiotemporal Dynamics of Wetland in Dongting Lake Based on Multi-Source Satellite Observation Data during Last Two Decades.

Monitoring the dynamics of wetland resources has practical value for wetland protection, restoration and sustainable utilization. Dongting Lake wetland reserves are well known for both their intra-annual and inter-annual dynamic changes due to the effects of natural or human factors. However, most wetland monitoring research has failed to consider the seasonal wetlands, which is the most fragile wetland type, requiring more attention. In this study, we used multi-source time series remote sensing data to monitor three Dongting Lake wetland reserves between 2000 and 2020, and the seasonal wetlands were separated from permanent wetlands. Multispectral and indices time series were generated at 30 m resolution using a two-month composition strategy; the optimal features were then selected using the extension of the Jeffries-Matusita distance (JBh) and random forest (RF) importance score; yearly wetland maps were identified using the optimal features and the RF classifier. Results showed that (1) the yearly wetland maps had good accuracy, and the overall accuracy and kappa coefficients of all wetland maps from 2000 to 2020 were above 89.6% and 0.86, respectively. Optimal features selected by JBh can improve both computational efficiency and classification accuracy. (2) The acreage of seasonal wetlands varies greatly among multiple years due to inter-annual differences in precipitation and evaporation. (3) Although the total wetland area of the three Dongting Lake wetland reserves remained relatively stable between 2000 and 2020, the acreage of the natural wetland types still decreased by 197.0 km2, and the change from natural wetland to human-made wetland (paddy field) contributed the most to this decrease. From the perspective of the ecological community, the human-made wetland has lower ecological function value than natural wetlands, so the balance between economic development and ecological protection in the three Dongting Lake wetland reserves requires further evaluation. The outcomes of this study could improve the understanding of the trends and driving mechanisms of wetland dynamics, which has important scientific significance and application value for the protection and restoration of Dongting Lake wetland reserves.