A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

Duan, Yueming; Zhang, Wenyi; Huang, Peng; He, Guojin; Guo, Hongxiang

doi:10.3390/rs13224576

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…In addition, the phenomenon of homospectral foreign matters caused by ground objects with similar spectral reflection characteristics to surface water (e.g., cloud/cloud shadows) would further lead to misclassification. At present, the commonly used methods of extracting surface water are to generate land cover maps using different classifiers to extract surface water, such as spectral-based supervised classification (e.g., support vector machines [27]), decision tree classification based on expert knowledge [28], object-oriented image classification [29] and deep learning image classification (e.g., convolutional neural networks [30]). Recently, cloud computing has enabled researchers to access and analyze massive amounts of remote sensing image data in the cloud without the need to download image data to a local computer.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

Zhang,

2024

Water

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In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

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Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

Zhang,

2024

Water

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“…Several multispectral satellites have been extensively used for water body extraction. These include the Landsat series, MODIS, Sentinel-2, and Chinese Gaofen series [1,[14][15][16][17]. These satellites provide rich spectral information, broad swath coverage, and short revisit periods, making them ideal for mapping large-scale surface water.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

Chen,

Wang,

Wang

et al. 2024

Remote Sensing

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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

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“…For example, Lu et al (2022) proposed a neighbor feature aggregation network for weakly supervised water extraction from high-resolution remote sensing imagery. Duan et al (2021) proposed a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information from GaoFen-1D satellite images. Hu et al (2022) also adopted the CNN model to extract rich features for water body segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Based on CNN and Multi-Scale Transformer for Extracting Water Bodies From High Resolution Remote Sensing Images

Zhang,

Hu,

Xiao

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Extracting water bodies from high-resolution remote sensing images has always been a challenging and hot task in the field of remote sensing. Considering that the accuracy and reliability of water body extraction still have some room for improvement, this paper proposes a hybrid network model based on CNN and multi-scale transformer for water body extraction from high-resolution remote sensing images. Specifically, the proposed network first uses a CNN model to extract a series of multi-scale features from shallow to deep from remote sensing images. These multi-scale features are then fed into a designed multi-scale transformer module to extract global contextual association information of water bodies. Afterwards, the water separability in the new multi-scale features output from the multi-scale transformer module is evaluated separately, and the features at different scales are adaptively weighted and fused according to their water separability. Subsequently, the network adaptively refines the fused features with the aid of a hybrid attention model to generate refined features that can effectively distinguish between water bodies and non-water bodies. Finally, these refined features are input into the prediction head to generate the final water body extraction results. The proposed network integrates the ability of CNN to capture local detail features and the ability of transformer to model global contextual semantic associations in a large range. Therefore, it can more accurately identify water bodies in remote sensing images, and the extracted water body boundaries have high accuracy and continuity. Finally, water body extraction experiments on the public dataset demonstrate the effectiveness of the proposed network. Moreover, the results of comparative experiments also show that compared with existing networks or methods such as U-Net, FCN8s, DeepLabv3+, and MSFA-Net, the proposed network has certain advantages in terms of water body extraction accuracy.

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A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

Cited by 14 publications

References 35 publications

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

A Novel Hybrid Model Based on CNN and Multi-Scale Transformer for Extracting Water Bodies From High Resolution Remote Sensing Images

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