Hyperspectral Remote Sensing Research Articles

Monitoring Cadmium Content in the Leaves of Field Pepper and Eggplant in a Karst Area Using Hyperspectral Remote Sensing Data

The ability to quickly and non-destructively monitor the cadmium (Cd) content in agricultural crops is the basic premise of effective prevention and control of Cd contamination in agricultural products. Hyperspectral technology provides a solution for this issue. The potential capability for the spectral prediction of the Cd content in the leaves of pepper and eggplant in the field was explored, and a spectral prediction model of the Cd content in these leaves was established. In this study, based on the indoor spectrum, the sensitive wavebands for predicting the Cd content in leaves were determined preliminarily by correlation analysis. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to establish spectral prediction models, and the final sensitive wavebands were determined by the size of the model index. The results show that the SVMR model exhibited higher prediction accuracy than the PLSR model. The RPDp (relative percent different of prediction set) values of the best SVMR prediction models for the pepper leaves and the eggplant leaves were 1.82 and 1.49, respectively. The values of Rp2 (coefficient of determination of prediction set), which can quantitatively estimate the Cd content in leaves, were 0.897 (p < 0.01) and 0.726 (p < 0.01), respectively. This study demonstrated that the leaf spectra of pepper and eggplant in the field can be used to predict the Cd content in leaves, providing a reference for monitoring the Cd content in the fruits of pepper and eggplant in the future.

Radiometric cross-calibration of Sentinel-2B MSI with HY-1C SCS based on the near simultaneous imaging of common ground targets.

To simplify the cross-calibration process and improve calibration frequency and accuracy, this paper proposes a cross-calibration method for the multispectral remote sensor Multi-Spectral Instrument (MSI) carried by Sentinel-2B using the hyperspectral remote sensor, that is, the satellite calibration spectrometer (SCS) carried by Hai Yang (HY)-1C, as the reference sensor and establishes the calibration process. Precise spectral response matching between SCS and MSI spectral channels is performed by the interpolation and iteration of hyperspectral data to eliminate the difference in band settings and significantly improve the accuracy of cross-calibration coefficients. The SNO-x inherited from the simultaneous nadir overpass (SNO) method is used as a prediction method to carry out cross-calibration imaging in mid- and low-latitude regions, which improves the cross-calibration frequency and broadens the dynamic range of calibration. The cross-calibration coefficients and offsets of MSI B1∼B7 and B8a were obtained by processing the earth observation images of the MSI and SCS on January 24, 2019. Then, the cross-calibration coefficients and offsets are applied to the ocean, farmland and other ground objects with different reflectance, and the reliability and accuracy of the cross-calibration results are evaluated with the Moderate-resolution Imaging Spectroradiometer (MODIS) carried by Terra as a reference. To improve the accuracy of the evaluation, the spectral band adjustment factor between the corresponding channels of MSI and MODIS is used to correct the measured reflectance of MODIS based on the satellite calibration coefficient. The reflectance directly obtained by processing the MSI image is used as the MSI-measured reflectance, the reflectance obtained based on the cross-calibration coefficient is used as the MSI-calculated reflectance, and the reflectance corrected by the spectral band adjustment factor (SBAF) is used as the MODIS-calculated reflectance. The results show that the mean root-mean-square relative error (RMSRE) between the MODIS-calculated reflectance and the MSI-calculated reflectance is 2.16% and that the mean RMSRE between the MODIS-calculated reflectance and the MSI-measured reflectance is 3.05%, indicating that the reflectance corrected based on calibration coefficients is closer to the MODIS-calculated reflectance. Finally, each uncertainty source in the cross-calibration is analyzed, and the comprehensive uncertainty is found to be 4.03%, indicating that SCS can be used as a reference for MSI cross-calibration.

Exploration and Practice of the Construction and Teaching Reform of the Model Course on the Application of Hyperspectral Remote Sensing

The course Hyperspectral Remote Sensing is professional, practical and advanced, which is a bridge course connecting the frontier technology of remote sensing and innovative practice. In this study, According to the "Golden Course" construction standards of "innovation, high order, and challenge", considering the characteristics of students and the course, taking full advantage of information-based teaching, combining the new technology and application of hyperspectral remote sensing, the teaching and experimental program of the course is formulated and optimized continuously by the cooperation between the university and enterprises; Under the support of the cooperative education platform of the university and enterprises, the experimental content of the course is designed based on the domestic PIE series software. Based on the concept of "student-centered", this course adopts a problem-oriented approach and integrates innovative teaching modes such as case-based, seminar-based, literature-reading and presentation-based into the traditional teaching mode. The teaching objectives of the course are mainly achieved through online and offline hybrid teaching, university-enterprise cooperation for cultivating innovative practical skills, higher-order thinking and presentation research. The reform of this course has strengthened students' understanding of research frontiers, encouraged them to apply their knowledge flexibly, increased their ability to explore and solve problems actively, and incubated a large number of achievements in the innovation and entrepreneurship practical training and professional skills competition.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean.

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.

Improved Three-Dimensional Inception Networks for Hyperspectral Remote Sensing Image Classification

Convolutional neural networks (CNNs) have been applied to the categorization of hyperspectral images. Typically, CNNs employ fixed convolution kernel sizes. This may be complicated by the high-dimensional features and the spatial-spectral features variability of hyperspectral data. In such situation, dealing with various dataset types flexibly is hampered by the fixed kernel size. Therefore, we chose a parallel design, an upgraded three-dimensional (3D) inception network, for hyperspectral image classification. Additionally, the adaptive band selection of hyperspectral remote sensing data employs dimensionality reduction based on interactive information entropy, allowing the selection of low-redundant bands with more abundant and discriminative information. Experimental results based on the publicly available hyperspectral datasets demonstrated that the enhanced 3D inception network can perform well with limited training samples. Moreover, the combinations of different dimensional convolutions in the 3D-2D and 3D-2D-1D inception networks achieved comparable classification results. According to the experimental findings, the suggested networks are generalised classification models with high classification rates.

A Novel Hyperspectral Remote Sensing Technique with Hour-Hectometer Level Horizontal Distribution of Trace Gases: To Accurately Identify Emission Sources

High spatial-temporal resolution distribution of atmospheric gaseous pollutant is an important basis for tracing its emission, transport, and transformation. Typical methods for acquiring regional atmospheric gaseous pollutant distributions are satellite remote sensing and in situ observations. However, these approaches have limitations, such as sparse overpass times for satellites and restricted coverage for in situ monitoring. In this study, we propose a method for the long-term detection of the horizontal distribution of trace gases. This method based on effective optical paths (EOPs) as the instrument's detection range. It acquires the average trace gas concentration along the EOPs by utilizing different detection distances within the ultraviolet (UV) and visible (VIS) spectral bands. Subsequently, we use the onion-peeling method to obtain trace gas concentrations at two distinct distances. The obtained trace gas horizontal distribution was consistent with the in situ and mobile measurements. Compared with satellite remote sensing, this method achieved horizontal distribution results with higher spatial and temporal resolutions, and located several small high-value areas in Hefei, China. The tropospheric NO 2 vertical column density (VCD) results of the satellite at transit time (13:30) were consistent with the hyperspectral NO 2 horizontal distribution results at 13:00 to 14:00 on the same day but were not consistent with the daily average NO 2 results. The hourly NO 2 concentration in each area was 10% to 40% lower than the daytime average obtained by the hyperspectral remote sensing result. We evaluated the errors associated with the calculation of NO 2 emissions based on the satellite results and found a bias of approximately 69.45% to 83.34%. The spatial distribution of NO 2 concentration obtained from MAX-DOAS measurements may help in future bottom-up emission calculations.

Application of hyperspectral and multispectral datasets for mineral mapping

In this study, hyperspectral datasets are simulated from multispectral data using a spectral reconstruction approach which is a sensor-independent technique. This technique makes use of information from atmospherically corrected multispectral Remote Sensing (MRS) data and normalized ground spectra for the simulation of HRS data. In this study EO-1, the ALI dataset was used for the simulation of hyperspectral Remote Sensing (HRS) data to discover the Udaipur region’s unique minerals. A total of 61 spectral bands with 10 nm bandwidth were simulated. The simulated HRS data were validated using visual interpretation, statistical and classification approaches. Simulated HRS data from EO-1 Advanced Land Imager (ALI) has shown a high correlation with EO-1 Hyperion data. Spectral Angle Mapper (SAM) classification was also performed on simulated hyperspectral data for mineral mapping. It was observed that simulated hyperspectral data have shown comparable results with Hyperion and are better than their corresponding multispectral datasets.

Unsupervised Domain Adaptive Classification for Hyperspectral Remote Sensing by Adversary Coupled with Distillation

Unsupervised Domain Adaptive Classification for Hyperspectral Remote Sensing by Adversary Coupled with Distillation

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

Ground-Based Hyperspectral Stereoscopic Remote Sensing Network: A Promising Strategy to Learn Coordinated Control of O3 and PM2.5 over China

With the coming of the “14th Five-Year Plan”, the coordinated control of particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM 2.5 ) and O 3 has become a major issue of air pollution prevention and control in China. The stereoscopic monitoring of regional PM 2.5 and O 3 and their precursors is crucial to achieve coordinated control. However, current monitoring networks are currently inadequate for monitoring the vertical profiles of both PM 2.5 and O 3 simultaneously and support air quality control. The University of Science and Technology of China (USTC) has established a nationwide ground-based hyperspectral stereoscopic remote sensing network based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) since 2015. This monitoring network provides a significant opportunity for the regional coordinated control of PM 2.5 and O 3 in China. One-year vertical profiles of aerosol, NO 2 and HCHO monitored from four MAX-DOAS stations installed in four megacities (Beijing, Shanghai, Shenzhen, and Chongqing) were used to characterize their vertical distribution differences in four key regions, Jing–Jin–Ji (JJJ), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB), respectively. The normalized and yearly averaged aerosol vertical profiles below 400 m in JJJ and PRD exhibit a box shape and a Gaussian shape, respectively, and both show exponential shapes in YRD and SB. The NO 2 vertical profiles in four regions all exhibit exponential shapes because of vehicle emissions. The shape of the HCHO vertical profile in JJJ and PRD was Gaussian, whereas an exponential shape was shown in YRD and SB. Moreover, a regional transport event occurred at an altitude of 600–1000 m was monitored in the southwest–northeast pathway of the North China Plain (NCP) by five MAX-DOAS stations (Shijiazhuang (SJZ), Wangdu (WD), Nancheng (NC), Chinese Academy of Meteorological Sciences (CAMS), and University of Chinese Academy of Sciences (UCAS)) belonging to the above network. The aerosol optical depths (AOD) in these five stations decreased in the order of SJZ > WD > NC > CAMS > UCAS. The short-distance regional transport of NO 2 in the 700–900 m layer was monitored between WD and NC. As an important precursor of secondary aerosol, the peak of NO 2 air mass in WD and NC all occurred 1 h earlier than that of aerosol. This was also observed for the short-distance regional transport of HCHO in the 700–900 m layer between NC and CAMS, which potentially affected the O 3 concentration in Beijing. Finally, CAMS was selected as a typical site to determine the O 3 –NO x –volatile organic compounds (VOCs) sensitivities in vertical space. We found the production of O 3 changed from predominantly VOCs-limited conditions to mainly mixed VOCs–NO x -limited condition from the 0–100 m layer to the 200–300 m layer. In addition, the downward transport of O 3 could contribute to the increase of ground surface O 3 concentration. This ground-based hyperspectral stereoscopic remote sensing network provide a promising strategy to support management of PM 2.5 and O 3 and their precursors and conduct attribution of sources.

Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review

Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed.

Research on Precise Fertilization Method of Rice Tillering Stage Based on UAV Hyperspectral Remote Sensing Prescription Map

Tillering fertilization is an important part of field management in rice production. As the first peak fertilizer requirement period of rice, tillering fertilization directly affects the number of tillers and the growth of rice in the middle and late stages. In order to investigate a method of constructing an accurate fertilizer prescription map in the tillering stage using an unmanned aerial vehicle (UAV) remote sensing nitrogen demand diagnosis and reduce the amount of chemical fertilizer while ensuring the rice yield, this study realized the diagnosis of the rice nitrogen nutrient demand using UAV hyperspectral remote sensing during the tilling stage fertilization window. The results showed that the fertilizer amount was determined using the characteristic waveband and remote sensing. The results showed that five rice hyperspectral variables were extracted in the range of 450–950 nm by the feature band selection and feature extraction for the inversion of rice nitrogen content, and the inversion model of rice nitrogen content constructed by the whale-optimized extreme learning machine (WOA-ELM) was better than that constructed by the whale-optimized extreme learning machine (ELM). The model coefficient of determination was 0.899 and the prescription map variable fertilizer application method based on the nitrogen content inversion results reduced the nitrogen fertilizer by 23.21%. The results of the study can provide data and a model basis for precise variable fertilizer tracking by agricultural drones in the cold rice tillering stage.

Using Hyperspectral Remote Sensing to Monitor Water Quality in Drinking Water Reservoirs

At the Blankaart Water Production Center, a reservoir containing 3 million m3 of raw surface water acts as a first biologic treatment step before further processing to drinking water. Over the past decade, severe algal blooms have occurred in the reservoir, hampering the water production. Therefore, strategies (e.g., the injection of algaecide) have been looked at to prevent these from happening or try to control them. In this context, the HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR), installed since early 2021, helps in monitoring the effectiveness of these strategies. Indeed, the HYPSTAR provides, at a very high temporal resolution, bio-optical parameters related to the water quality, i.e., Chlorophyll-a (Chla) concentrations and suspended particulate matter (SPM). The present paper shows how the raw in situ hyperspectral data (a total of 8116 spectra recorded between 2021-02-03 and 2022-08-03, of which 2988 spectra passed the quality check) are processed to find the water-leaving reflectance and how SPM and Chla are derived from it. Based on a limited number of validation data, we also discuss the potential of retrieving phycocyanin (an accessory pigment unique to freshwater cyanobacteria). The results show the benefits of the high temporal resolution of the HYPSTAR to provide near real-time water quality indicators. The study confirms that, in conjunction with a few water sampling data used for validation, the HYPSTAR can be used as a quick and cost-effective method to detect and monitor phytoplankton blooms.

Monitoring Marine Oil Spills in Hyperspectral and Multispectral Remote Sensing Data by the Spectral Gene Extraction (SGE) Method

Oil spill incidents threaten the marine ecological environment. Detecting sea surface oil slicks by remote sensing images provides support for the efficient treatment of oil spills. This is important for sustainable marine development. However, traditional methods based on field analysis are time-consuming. Spectral indices lack applicability. In addition, traditional machine learning methods strictly rely on training and testing samples which are in short supply in oil spill images. Inspired by the spectral DNA encoding method, a spectral gene extraction (SGE) method was proposed to detect oil spills in hyperspectral images (HSI) and multispectral images (MSI). The SGE method contained a parameter and two strategies. The parameter of elimination was designed based on the population genetic frequency. It was used to control the number of spectral genes. The spectral gene extraction strategies, named largest in-class similarity (LIS) strategy and largest inter-class difference (LID) strategy, were proposed to mine the spectral genes by oil spill samples. The oil spills would be determined by calculating the similarity of the extracted spectral genes to the DNA encoded images. In this research, the SGE method was validated by two AVIRIS images of the Gulf of Mexico oil spill, one MODIS image of the Gulf of Mexico oil spill, and one Landsat 8 image of a Persian Gulf oil spill. The oil spills in different remote sensing images could be detected accurately by the proposed method in a small set of samples. Experimental results indicated that the proposed method was suitable for detecting marine oil spills in AVIRIS, MODIS, and Landsat 8 images. In addition, the SGE method with the LIS strategy was more suitable for detecting oil spills in HSI. Its proper elimination rates were 0.8~1.0. The SGE method with the LID strategy was more suitable for detecting oil spills in MSI. Its proper elimination rates were 0.5~0.7.

Improved chimp optimization with deep transfer learning enabled soil classification technique using hyperspectral remote sensing images

Hyperspectral remote sensing (HRS) is commonly employed for crop and soil mapping. Soil and crop classification mapping acts as major roles in planning and management of sustainable development. Thus, it is necessary to design automated crop and soil prediction techniques using HRS images. The recent developments of machine learning (ML) and deep learning (DL) enable to design of effective classification models for soil and crops. This study effectively introduces improved chimp optimization with deep transfer learning enabled crop and soil classification (ICODTL-CSC) technique using HRS images. The presented ICODTL-SC technique aims to identify and categorize different types of crops and soil. To accomplish this, the ICODTL-CSC technique incorporates Adagrad with EffcientNetB0-based feature extractor for producing a collection of feature vectors. In addition, Improved Chimp Optimization Algorithm with a deep adaptive wavelet network model is applied for soil classification, showing the novelty of our work. The performance validation of the presented method is performed using Tamil Nadu Hills dataset, Pavia University, and Salinas scene datasets. The experimental outcomes indicated that the presented method has exhibited the existing techniques in terms of different performance measures.

Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work.

Evaluation of machine learning techniques with AVIRIS-NG dataset in the identification and mapping of minerals

Identifying valuable mineral resources is obligatory for human development and survival due to their scientific and economic implications. Hyperspectral Remote Sensing (HRS) is an efficient technique that empowers us to precisely identify and map altered minerals based on spectral absorption curves in the VNIR and SWIR range of the electromagnetic spectrum. Machine Learning Algorithms (MLAs) are a subclass of Artificial Intelligence (AI) that improves and automate HRS based lithological mapping using spectra based classification approach. The present study evaluates various MLAs in identifying and mapping hydrothermally altered and weathered minerals such as kaolinite, talc, kaosmec, and montmorillonite. The study was performed with the Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) hyperspectral dataset of Jahazpur town of Bhilwara district in the state of Rajasthan, India (75° 9′ 57′' E, 25° 32′ 24″ N). The Spectral Angle Mapper (SAM) algorithm was applied to create a reference mineral distribution map for the target mineral classes. Further, the reference map has been verified with the field validation survey. A total of 828 pixels were identified as end member pixels. The obtained endmember pixels were further used for developing predictive machine learning based models for mapping the mineral resources using eight supervised classifiers, namely Support Vector Machine, k-Nearest Neighbor, Decision Tree, Random Forest, Logistic Regression, Artificial Neural Network, Linear Discriminant Analysis, and Naïve Bayes. The performance of the classifiers was measured using the kappa coefficient, overall accuracy, precision, recall, F1-score, Matthews’s correlation coefficient, and ROC curve. It was observed in the study that the Support Vector Machine outperformed all the classifiers in terms of overall accuracy and AVIRIS-NG data poses an excellent potential for mineral mapping using machine learning based models in a diverse mountainous area.

Epidemic of Wheat Stripe Rust Detected by Hyperspectral Remote Sensing and Its Potential Correlation with Soil Nitrogen during Latent Period.

Climate change affects crops development, pathogens survival rates and pathogenicity, leading to more severe disease epidemics. There are few reports on early, simple, large-scale quantitative detection technology for wheat diseases against climate change. A new technique for detecting wheat stripe rust (WSR) during the latent period based on hyperspectral technology is proposed. Canopy hyperspectral data of WSR was obtained; meanwhile, duplex PCR was used to measure the content of Puccinia striiformis f.sp. tritici (Pst) in the same canopy section. The content of Pst corresponded to its spectrum as the classification label of the model, which is established by discriminant partial least squares (DPLS) and support vector machine (SVM) algorithm. In the spectral region of 325–1075 nm, the model’s average recognition accuracy was between 75% and 80%. In the sub-band of 325–1075 nm, the average recognition accuracy of the DPLS was 80% within the 325–474 nm. The average recognition accuracy of the SVM was 83% within the 475–624 nm. Correlation analysis showed that the disease index of WSR was positively correlated with soil nitrogen nutrition, indicating that the soil nitrogen nutrition would affect the severity of WSR during the latent period.

Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches.