WorldView-2 Images Research Articles

Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity

ContextOften called a ‘Garden city’, Bengaluru is renowned for its green infrastructure. However, the association of human wellbeing with the amount of tree cover (as an example of green infrastructure), degree of urbanization, and local people's socio-demographics has not been explored.ObjectiveWe investigated how human wellbeing is related to the amount of tree cover at household and neighborhood levels, the degree of urbanity, and underlying social factors among respondents.MethodsThe study was conducted in 61 towns along the rural–urban gradient of Bengaluru, surveying 836 respondents in total. Data on multiple dimensions of respondents’ wellbeing was collected between December 2016 and May 2017. The percentage of tree cover at the household and neighborhood levels was obtained based on WorldView-3 images. Logistic regression models were applied to assess correlations between human wellbeing and tree cover. Kruskal–Wallis tests, Mann–Whitney tests, and cross tabulation with chi-square tests were conducted to investigate relations of human wellbeing with the degree of urbanity, and with social factors.ResultsWe found that several variables of human wellbeing were positively associated with tree cover. Measures of wellbeing also differed along the rural–urban gradient and among social groups.ConclusionsThough urbanization is often considered to promote material wellbeing, non-material aspects (e.g., community activities and social relationships) are also important components of overall human wellbeing in urbanizing landscapes. Holistic interactions among natural, spatial, and social factors should be considered while designing interventions for equitable urban landscapes that promote human wellbeing and ecological restoration.

DETERMINING SHALLOW WATER BATHYMETRY BY STEREO PHOTOGRAMMETRY TECHNIQUE USING WORLDVIEW-2 IMAGERY

Bathymetry in shallow-water areas plays an important role in marine environment management, marine economic resource development, and national defense and security. The echo sounding method is currently highly accurate; however, it has to deal with a variety of challenges as a result of the high cost, terrain influence, and meteorological conditions at sea. As remote sensing technology progresses, estimating bathymetry using satellite imagery will help lower measuring costs and expand monitoring coverage, particularly in shallow water, offshore areas, and difficult-to-access areas. In this article, the authors present a technique for determining bathymetry in shallow water regions by utilizing WorldView-2 stereoscopic images. The accuracy of stereoscopic depth measurement using WorldView-2 images is assessed by field depth measurement using the single-beam echo sounder. The study area is Nam Yet Island. The coefficient of determination (R2) between the two datasets (field measurement data and using satellite images) is 0.9. The stereoscopic satellite image method of depth measurement is highly accurate for regions with depths below 5 meters. The accuracy of the stereoscopic measurement decreases as the depth increases by more than 5 meters.

Constraining the Geometry of NeRFs for Accurate DSM Generation from Multi-View Satellite Images

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF’s superiority in accurate DSM generation from multi-view satellite images.

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

Mapping Seagrass Percent Cover And Biomass In Nusa Lembongan, Bali, Indonesia

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R2, RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R2 using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R2, RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

Mapping Dissolved Organic Carbon and Organic Iron by Comparing Deep Learning and Linear Regression Techniques Using Sentinel-2 and WorldView-2 Imagery (Byers Peninsula, Maritime Antarctica)

Byers Peninsula is considered one of the largest ice-free areas in maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving the effects of climate change on biological life cycles, limnology, and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical, and morphological characteristics that are strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. Mapping and monitoring those areas that are highly occupied by various species could be very useful to create models prepared from satellite images of the edaphic properties. In this approach, deep learning and linear regression models of the soil properties and spectral indexes, which were considered as explicative variables, were used. We trained the models on soil properties closely related to biological activity such as dissolved organic carbon (DOC) and the iron fraction associated with the organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe, and pH by training the linear regression and deep learning models on Sentinel-2 and WorldView-2 images. The most robust models, the pH model built with the deep learning approach on Sentinel images (MAE of 0.51, RMSE of 0.70, and R2 with a residual of −0.49), the DOC model built with linear regression on Sentinel images (MAE of 189.39, RMSE of 342.23, and R2 with a residual of 0.0), and the organic Fe model built with deep learning (MAE of 116.20, RMSE of 209.93, and R2 of −0.05), were used to track possible areas with ornithogenic soils, as well as areas of Byers Peninsula that could be supporting the highest biological development.

Multi-objective optimization for worldview image segmentation funded on the entropies of havrda–charvat and exponential

Multi-objective optimization for worldview image segmentation funded on the entropies of havrda–charvat and exponential

Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630–690 nm), green (510–580 nm), and near-infrared 1 (770–895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY

Abstract. Image segmentation, is of utmost importance in the disciplines of digital image processing, particularly remote sensing and computer vision, has seen an increasing demand for precise and efficient algorithms. This study focuses to conduct a comparative exploration of the segmentation capabilities of two sophisticated techniques namely, Multiresolution Segmentation (MRS) and Segment Anything Model (SAM), leveraging the high-resolution WorldView-3 (WV-3) satellite image. MRS adopts a hierarchical methodology, segmenting an image into various scales while retaining a profound understanding of its structure. Conversely, SAM employs a deep learning algorithm, prioritizing segment creation based on conceptual pixel similarity, irrespective of spatial adjacency. The WV-3 image, featuring diverse land cover elements like agricultural parcels, industrial structures, roads, red roofs, single trees, and water bodies, serves as the basis for assessing segmentation quality. Both methods are applied to the image, and their outcomes are individually evaluated against manually generated polygonal land use/cover objects. Segmentation quality metrics are employed for assessment. Results reveal MRS effectively preserves fine details and entity delineation, while SAM excels in capturing contextually similar regions. MRS outperforms SAM with a negligible discrepancy, yet SAM demonstrates superiority in the red roof object, achieving an Intersection over Union (IoU) value of 0.70 compared to MRS’s 0.49. MRS tends to generate numerous segments for an item, while SAM produces only one segment. Nevertheless, it is important to recognise that both algorithms have specific constraints in particular scenarios, such as excessive segmentation in areas with abundant texture or inadequate segmentation in areas with slight changes.

Automatic differentiation of Eucalyptus species through Sentinel-2 images, Worldview-3 images and LiDAR data

Eucalyptus constitutes one of the most common tree genera used in forest plantations worldwide. In Europe, Eucalyptus trees are especially common in the Northwest of the Iberian Peninsula, E. nitens and E. globulus being the most commonly cultivated species. Each species presents particularities that lend to different exploitation strategies and industrial usages. Therefore, updated knowledge about the abundance and spatial distribution of the different species is important for forest planning. This is a special challenge for areas where forest land is highly fragmented. Remote sensing has been used to efficiently monitor the distribution of the Eucalyptus genera, however little research has been able to map specific Eucalyptus species. This study evaluates the efficiency of Sentinel-2 data, Worldview-3 images, and Airborne LiDAR data in the differentiation of E. nitens and E. globulus. Supervised classifications were performed using neural networks for these data sets both individually and in combination. The highest accuracies were obtained when using Sentinel-2 data in combination with LiDAR point clouds and when using Sentinel-2 data in a multitemporal approach. The best time of year to differentiate between the two species is during the emergence of spring shoots. Worldview-3 images have a moderate capacity to differentiate between the two species, although this is increased when textural metrics are included. This study can serve as the basis for generating Eucalyptus species distribution maps, which will allow for improved forest management and planning.

A SHALLOW NEURAL NETWORK MODEL FOR URBAN LAND COVER CLASSIFICATION USING VHR SATELLITE IMAGE FEATURES

Abstract. Recently, image classification techniques using neural networks have received considerable attention in sustainable urban development, since their applications have an extreme effect on building distribution, infrastructural networks, and water resource management. In this research, a back-propagation shallow neural network model is presented for very high resolution satellite image classification in urban environments. Workflow procedures consider selecting and collecting data, preparing required study areas, extracting distinctive features, and applying the classification process. Visual interpretation is performed to identify observed land cover classes and detect distinctive features in the urban environment. Pre-processing techniques are implemented to present the used images in a more suited form for the classification techniques. A shallow neural network model (supported by MathWorks MATLAB environment) is successfully applied and results are evaluated. The proposed model is tested for classifying both WorldView-2 and WorldView-3 multispectral images with different spatial and spectral characteristics to check the model’s applicability to various kinds of satellite imagery and different study areas. Model outcomes are compared to two well-known classification methods; the Nearest Neighbour object-based method and the Maximum Likelihood pixel-based classifier, to validate and check the model stability. The overall accuracy achieved by the proposed model is 86.25% and 83.25%, while the nearest neighbour approach has obtained 84.50% and 82.75%, and the maximum likelihood classifier has accomplished 82.50% and 80.25% for study area 1 and study area 2 respectively. Obtained results indicate that the developed shallow neural network model achieves a promising accuracy for urban land cover classification in comparison with the standard techniques.

Species Classification and Carbon Stock Assessment of Mangroves in Qi’ao Island with Worldview-3 Imagery

Mangroves play a substantial role in the global carbon cycle and are highly productive. To evaluate the effectiveness of a remote-sensing image in mangrove-species classification and carbon stock assessment, we utilized Worldview-3 images to map the mangrove species in Qi’ao Island, Guangdong Province, China, using a Random Forest classifier. We compared the contribution of spectral features, derivation features, and textural features to the classification accuracy and found that textural features significantly improved the overall accuracy, achieving 92.44% with all features combined. According to field-survey results, the main mangrove species in Qi’ao Island were Sonneratia apetala (SA), Acanthus ilicifolius (AI), Kandelia candel (KC), Acrostichum aureum (AA), Aegiceras corniculatum (AC), and Heritiera littoralis (HL); there are also many reeds mixed with mangroves. According to classification results, the total area of the mangroves and reeds is about 451.86 ha; the SA was the dominant species with an area of 393.90 ha. We calculated the total carbon stock of mangroves on Qi’ao Island by integrating the area of different species and their average total carbon density for the first time. The total carbon stock of mangroves in Qi’ao Island is between 147.78–156.14 kt, which demonstrates the significant potential of mangroves in carbon sequestration.

Satellite image fusion using cyclic spatio-spectral GAN model

ABSTRACT Preserving spectral and spatial information in the satellite image fusion problem is one of the essential challenges. This paper presents a GAN-based method for fusing panchromatic and multispectral satellite images. The proposed method utilises the idea of Cycle-GAN with two generators, for spectral and spatial information preservation, based on residual in residual dense block super-resolution architecture. First, generator-1 translates panchromatic and multispectral satellite images to a high-resolution fused image and then preserves details by utilising generator-2. The goal is to reach the spatial and spectral details of panchromatic and multispectral satellite images, respectively. Two discriminators are employed, one for spectral and the other for spatial transformations, and weighted L1 loss is used for both as cycle losses. By leveraging the unique capabilities of the two generators, the proposed method achieves high-quality image fusion results with improved spectral and spatial resolutions that are evaluated over two different datasets. The experimental results demonstrate the effectiveness of the proposed method, with enhancements ranging from approximately 2% to 30% and 0.5% to 35% in WorldView2 and GeoEye-1 images, respectively, in five metrics including PSNR. Moreover, we show significant enhancements of approximately 7% to 50% and 22% to 29% in the same datasets in metrics like SAM and ERGAS.

Identification of seagrass beds using high-resolution satellite imagery to basic inventory data supporting coastal ecosystem management (Case study: Lancang Island)

Seagrass habitat is an important element of the coastal marine ecosystem, as it influences all major fields in the coastal environments. Due to anthropogenic and natural pressure, such identification of habitat distribution becomes crucial. Remote sensing can provide the best solution regarding time and cost to extract seagrass beds’ spatial and temporal information. When the low spatial resolution satellite image provides low accuracy and high uncertainty of seagrass distribution, we assess the high spatial resolution image product, the WorldView-2 image, to identify the seagrass bed distribution in Lancang Island. We confidently found that higher resolution images could leverage the mapping quality, especially on the seagrass beds, which could be highly sensitive to miss-detection. We can produce 83,75% overall accuracy using pixel-based classification; this is better among other low-resolution images for Instants, Landsat, and Sentinel. Our results suggest that the WorldView-2 image could perform better on seagrass beds identification, yet further improvement on more robust classification methods and algorithms could amplify the mapping and monitoring strategy. Based on this, it can be used as basic data to support future coastal ecosystem management on Lancang Island.

Повышение эффективности текстурной сегментации лесного полога по изображениям сверхвысокого пространственного разрешения

The issue of increasing the accuracy of tree canopy segmentation structures in the processing of very high spatial resolution satellite images by joint use of textural features extracted by different methods is considered. The TTSPCA method (Total Texture Statistics Principal Component Analysis) is proposed and its effectiveness is shown for a number of test cases. Estimates of TTSPCA effectiveness are obtained for the forest stands segmentation and growth types. The panchromatic Worldview-2 image of the test area (Bronnitskoye forestry, Moscow region) obtained in summer was used as remote data. To perform texture segmentation, in addition to TTSPCA, several standard second-order statistical methods and a spectral (energy) method based on the wavelet transform were also used. It is shown that almost all considered statistical and spectral methods provide forest stand segmentation with errors not exceeding 3–4 %. It has been established that the TTSPCA method makes it possible to reduce the probability of errors in the forest inventory zone, as well as to identify sections with a predominance of natural and artificial plantations with an accuracy of over 85 %. The results obtained can be further recommended for use in order to improve the system we are developing for joint spectral-textural processing of satellite images with different spatial resolutions.

Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data

Urban reservoirs are important for drinking water services and urban living. However, potentially toxic cyanobacteria blooms are frequently present due to human pollution and might threaten the urban water supply. Conveniently, cyanobacteria can be monitored by remote sensing-based approaches based on the spectral features of C-Phycocyanin (PC). Furthermore, methods leveraging Machine Learning Algorithms (MLA) for PC estimation from hyperspectral data have highlighted the potential to estimate PC more accurately - even at low concentrations. Since relatively few methodologies for PC retrieval in tropical environments have been developed or validated, this research evaluated PRISMA hyperspectral data processed with three MLA (Random Forest, Extreme Gradient Boost, and Support Vector Machines) to estimate PC concentrations in the Billings reservoir, Brazil. The same MLA were used to generate PC models using Wordview-3 and Landsat-8/OLI simulated data to assess the potential gain of using hyperspectral over multispectral data. A PRISMA image was processed with three atmospheric correction methods and validated with co-located in-situ data, where the best atmospherically corrected product was used to generate synthetic Landsat-8/OLI and Worldview-3 images. The PC models were calibrated and validated through Monte Carlo simulation using field radiometric and biological data (Chlorophyll-a, PC, and phytoplankton taxonomy) collected in eight field campaigns (N = 115). The PRISMA and the synthetic multispectral images were used for a second round of models’ validation using co-located PC measurements (match-up window ± 4 h). The global PC Mixture Density Network was also applied to the PRISMA data, and the estimates were compared with the other MLA. The results showed that the standard PRISMA surface reflectance product provided the best atmospheric correction (MAE < 20% for the 500–700 nm bands), while ACOLITE and 6SV underperformed it from two to more than ten-fold. Cyanobacteria species were abundant in 96% of the taxonomical samples, even though relatively low PC concentrations were found (PC from 0 to 301.81 μg/L and median PC = 2.9 μg/L). The global Mixture Density Network sharply overestimated PC (MAE = 280% and Bias = 280%), potentially due to Billings reservoir’s low PC:Chlorophyll-a ratio relative to the original training dataset. PRISMA/Random Forest (MAE = 45%) achieved the lowest error for orbital PC estimate, while Extreme Gradient Boost outperformed the other MLA using Worldview-3 (MAE = 49%) and Landsat-8 (MAE = 74%) synthetic imagery. Therefore, the results suggest hyperspectral and multispectral orbital data aligned with MLA are feasible for monitoring PC, even for waters containing low PC concentrations and reduced PC:Chlorophyll-a ratios.

Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (I) methods and comparisons with actual data

The capability of spaceborne stereogrammetry using very high-resolution (VHR, <2 m) imagery with various environmental, experimental, and sensor configurations for characterizing forest canopy surfaces has not been completely explored. Existing archives of VHR imagery include a limited subset of potential stereo image acquisition configurations and may therefore exclude optimal configurations for capturing critical structural features of forest canopy surface. By contrast, simulated VHR imagery from 3-D radiative transfer models (RTM) can explore the full range of spatial, spectral, and sun-sensor configurations to identify factors that contribute to uncertainties in stereo-derived estimates of forest canopy structure. We developed a novel method to simulate VHR stereopairs using the discrete anisotropic radiative transfer (DART) model and then derive surface elevations from the simulated images. We reconstructed one open-canopy and one closed-canopy forest scene and created a reference digital surface model/digital terrain model (DSM/DTM) using airborne small-footprint lidar points over the study sites. The VHR simulations were configured to match three independent WorldView stereopairs. The results showed that, compared to the reference DSM, the surface elevations derived using simulated and WorldView image data were consistent, with differences of <1.6 m in vertical bias, < 1 m in root mean square error (RMSE), and < 0.07 in correlation coefficient (R). We demonstrated that realistic 3-D RTM simulations could be georeferenced with a camera model for DSM generation from simulated stereopairs. This work will support a follow-up investigation that examines stereo-derived DSM quality over a broad range of surface types and acquisition parameters to suggest optimal configurations for actual VHR stereo data acquisition of vegetation canopy surfaces.

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands.&#x0D; Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island&#x0D; &#x0D; Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Urban feature shadow extraction based on high-resolution satellite remote sensing images

With the continuous increase in the resolution of high-resolution remote sensing satellite images, shadows have increasingly impacted feature target recognition and classification in images, urban reconstruction, and image interpretation. Although various shadow detection methods exist for different features, undetected small shadowed areas, misclassified dark areas, and highlighted non-shadowed areas remain. Moreover, it is still difficult to distinguish waters from shadowed regions owing to their similar characteristics. To further solve these problems, we propose a novel mixed shadow detection index (MSDI) for extracting urban feature shadows, using the original information of the first principal component that contains the differences between most of the different objects in the image, as well as the difference features in the shadowed areas and water bodies. We evaluated the effectiveness and robustness of our method by conducting comparison experiments using Gaofen-1, Gaofen-2, and WorldView-3 images from different scenes, times, and acquisition locations. Through visual analysis and data analysis of our method, we found that the method achieved excellent shadow detection results, with an average total accuracy 94% for shadow detection. The proposed shadow detection algorithm could accurately recognize waters, and it could also accurately recognize easily missed small shadowed areas and easily confused non-shadowed areas.

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PDF