Robust water body extraction from landsat imagery by using gradual assignment of water index and DSM

Puttinaovarat, Supattra; Khaimook, Kanit; Polnigongit, Weerapong; Horkaew, Paramate

doi:10.1109/icsipa.2015.7412175

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…When water bodies are distinguished from other ground features using spectral characteristics, other methods can combine multiple characteristics to classify and identify them. Water body information extraction accuracy varies between algorithms [30,31]. For a given algorithm based on different data, the water body extraction accuracy will vary; therefore, using a combination of different algorithms and datasets will produce results with different accuracies [32].…”

Section: Introductionmentioning

confidence: 99%

Satellite Detection of Surface Water Extent: A Review of Methodology

Cao

et al. 2022

Water

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Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

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Section: Introductionmentioning

confidence: 99%

Satellite Detection of Surface Water Extent: A Review of Methodology

Cao

et al. 2022

Water

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Multi scale feature extraction network with machine learning algorithms for water body extraction from remote sensing images

Nagaraj

Kumar

2022

International Journal of Remote Sensing

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Water Body Extraction in Remote Sensing Imagery Using Domain Adaptation-Based Network Embedding Selective Self-Attention and Multi-Scale Feature Fusion

Liu

Wang

2022

Remote Sensing

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A water body is a common object in remote sensing images and high-quality water body extraction is important for some further applications. With the development of deep learning (DL) in recent years, semantic segmentation technology based on deep convolution neural network (DCNN) brings a new way for automatic and high-quality body extraction from remote sensing images. Although several methods have been proposed, there exist two major problems in water body extraction, especially for high resolution remote sensing images. One is that it is difficult to effectively detect both large and small water bodies simultaneously and accurately predict the edge position of water bodies with DCNN-based methods, and the other is that DL methods need a large number of labeled samples which are often insufficient in practical application. In this paper, a novel SFnet-DA network based on the domain adaptation (DA) embedding selective self-attention (SSA) mechanism and multi-scale feature fusion (MFF) module is proposed to deal with these problems. Specially, the SSA mechanism is used to increase or decrease the space detail and semantic information, respectively, in the bottom-up branches of the network by selective feature enhancement, thus it can improve the detection capability of water bodies with drastic scale change and can prevent the prediction from being affected by other factors, such as roads and green algae. Furthermore, the MFF module is used to accurately acquire edge information by changing the number of the channel of advanced feature branches with a unique fusion method. To skip the labeling work, SFnet-DA reduces the difference in feature distribution between labeled and unlabeled datasets by building an adversarial relationship between the feature extractor and the domain classifier, so that the trained parameters of the labeled datasets can be directly used to predict the unlabeled images. Experimental results demonstrate that the proposed SFnet-DA has better performance on water body segmentation than state-of-the-art methods.

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Robust water body extraction from landsat imagery by using gradual assignment of water index and DSM

Cited by 4 publications

References 24 publications

Satellite Detection of Surface Water Extent: A Review of Methodology

Satellite Detection of Surface Water Extent: A Review of Methodology

Multi scale feature extraction network with machine learning algorithms for water body extraction from remote sensing images

Water Body Extraction in Remote Sensing Imagery Using Domain Adaptation-Based Network Embedding Selective Self-Attention and Multi-Scale Feature Fusion

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