Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection

Yuan, Kunhao; Xu, Z.; Schaefer, Gerald; Feng, Jianxin; Guan, Lin; Fang, Hui

doi:10.1109/jstars.2021.3098678

Cited by 104 publications

(51 citation statements)

References 42 publications

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“…The results of the experiments that utilize multispectral input types are provided in Table IV. Even though there are a number of band fusion modules applied by slightly modifying the networks [104], [105], the motivation in this study is to implement only the original architectures so only U-Net, SegNet, and random forest are used. Although tested for a limited number of architectures, the results are consistent with three-channel input type experiments.…”

Section: B Resultsmentioning

confidence: 99%

Deep Semantic Segmentation of Trees Using Multispectral Images

Ülkü

Akagündüz

Ghamisi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Forests can be efficiently monitored by automatic semantic segmentation of trees using satellite and/or aerial images. Still, several challenges can make the problem difficult, including the varying spectral signature of different trees, lack of sufficient labelled data, and geometrical occlusions. In this paper, we address the tree segmentation problem using multispectral imagery. While we carry out large-scale experiments on several deep learning architectures using various spectral input combinations, we also attempt to explore whether hand-crafted spectral vegetation indices can improve the performance of deep learning models in the segmentation of trees. Our experiments include benchmarking a variety of multispectral remote sensing image sets, deep semantic segmentation architectures, and various spectral bands as inputs, including a number of hand-crafted spectral vegetation indices. From our large-scale experiments, we draw several useful conclusions. One particularly important conclusion is that, with no additional computation burden, combining different categories of multispectral vegetation indices, such as NVDI, ARVI, and SAVI, within a single three-channel input, and using the state-of-the-art semantic segmentation architectures, tree segmentation accuracy can be improved under certain conditions, compared to using high-resolution visible and/or nearinfrared input.

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Section: B Resultsmentioning

confidence: 99%

Deep Semantic Segmentation of Trees Using Multispectral Images

Ülkü

Akagündüz

Ghamisi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…RGB band imagery is the primary focus in substantial research for water body extraction, but many more bands are available in RS imagery. A multichannel water body detection network (MC-WBDN) was created in [ 47 ], which fused the infrared and RGB channels and used them as input data for their CNN architecture. They demonstrated that when multispectral data is used, model performance for water body detection is increased and the model is more robust to lighting conditions.…”

Section: The State Of the Art: Advances In Intelligent Waterbody Info...mentioning

confidence: 99%

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang

Driscol

Sarigai

et al. 2022

Sensors

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Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

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“…(Corresponding author: Guizhou Zheng.) Y. Zhao, G. Zheng, Z. Xu, and Z. Chen are with the School of Geography and Information Engineering, China University of Geosciences, Wuhan segmentation is to divide each pixel unit in HRRSIs into corresponding categories, which holds great significance in applications such as land use and land cover [2], [3], ecological system [4], [5], inversion of water depth [6], [7] and other industries. However, visual interpretation of HRRSIs is a method with low efficiency and strong dependence on knowledge and experience.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Weighted-Aggregating and Boundary Enhancement Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Zhao

Zheng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-resolution remote sensing images (HRRSIs) play an important role in large area and real-time earth observation tasks. However, HRRSIs typically comprise heterogeneous objects of various sizes and complex boundary lines, which poses challenges to HRRSI segmentation. Despite the fact that deep convolutional neural networks dramatically boosted the accuracy, several limitations exist in standard models. Existing methods, mainly concatenate multi-scale information to extract the various sizes of objects. However, these methods ignore differentiating information, making it difficult to take advantage of them and completely extract small objects. In addition, there have remained some difficulties in extracting boundary information with positions of uncertainty in previous works. In this paper, we propose a novel multi-scale feature weightedaggregating and boundary enhancement network (MFBE-Net) for the segmentation of HRRSIs. ResNet-50, possessing a strong ability to extract features, is employed as the backbone. To fully utilize the information that was extracted, we propose a multiscale feature weighted-aggregating module, which aims to weightintegrate deep features, shallow features, and global information. The boundary enhancement module is designed to solve the blurry boundary information problems and locate its positions. Coordinate attention is also applied in the framework to coherently label size-varied ground objects from different categories and reduce information redundancy. Meanwhile, a mixed loss function is used to supervise the network training process. Finally, MFBE-Net was verified on two public HRRSI datasets, and the experimental results show that the proposed framework outperformed other existing mainstream deep learning methods and could further improve the accuracy of HRRSI segmentation.

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Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection

Cited by 104 publications

References 42 publications

Deep Semantic Segmentation of Trees Using Multispectral Images

Deep Semantic Segmentation of Trees Using Multispectral Images

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Multiscale Feature Weighted-Aggregating and Boundary Enhancement Network for Semantic Segmentation of High-Resolution Remote Sensing Images

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