DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

Liu, Zhiheng; Chen, Xuemei; Zhou, Suiping; Yu, Hao; Guo, Jianhua; Liu, Yanming

doi:10.3390/rs14215567

Cited by 10 publications

(10 citation statements)

References 47 publications

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“…The proposed MFGF-UNet is compared with six other state-of-the-art approaches: FCN-8s [12], U-Net [14], DeepLabV3+ [19], BASNet [20], DUPNet [1] and SegNeXt [24]. For all the other comparison methods, the parameters are set according to the corresponding original work.…”

Section: Comprehensive Comparison With Other Methodsmentioning

confidence: 99%

“…Detection of surface water area is essential for water resource management, flood identification, and ecological protection [1]. Satellite remote sensing images have the advantages of large coverage, low cost and a short data acquisition period and are often used in water area analysis [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…BASNet generates coarse prediction maps using the encoder-decoder network and then refines the coarse prediction maps with the ResNet34 [21] refinement module to improve the performance. For solving some difficulties existing in water areas segmentation, such as complex backgrounds, huge scales, water connectivity, and rough edges, Liu et al [1] proposed Dense U-net Plus Network (DUPNet) in 2022, which is also based on the U-Net architecture. DUPNet integrates dense block modules from DenseNet [22] and Multi-scale Spatial Pyramid Pooling (MSPP) modules from DeeplabV3+.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

Wang,

Zhang,

Chen

et al. 2024

Remote Sensing

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Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.

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Section: Comprehensive Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

Wang,

Zhang,

Chen

et al. 2024

Remote Sensing

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“…To accurately fuse multi-scale contextual information, MSNANet [27] utilizes the MSNA module, establishing remote feature dependencies between channels to improve the accuracy and mapping capability of the model. In related work, DUPNet [28], a network structure with a U-shaped encoder and decoder, uses dense blocks to extract image semantic features and obtain highly abstract feature maps. Instead of employing the maximum pooling layer, it adopts Atrous Separable Convolution during down-sampling to increase the perceptual field of view of the feature maps, thereby enhancing the robustness of the network model.…”

Section: Cnn-based Semantic Segmentation For Water Body Extractionmentioning

confidence: 99%

An Enhanced Dual-Stream Network Using Multi-Source Remote Sensing Imagery for Water Body Segmentation

Zhang,

Geng,

Yang

et al. 2023

Applied Sciences

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Accurate surface water mapping is crucial for rationalizing water resource utilization and maintaining ecosystem sustainability. However, the diverse shapes and scales of water bodies pose challenges in automatically extracting them from remote sensing images. Existing methods suffer from inaccurate lake boundary extraction, inconsistent results, and failure to detect small rivers. In this study, we propose a dual-stream parallel feature aggregation network to address these limitations. Our network effectively combines global information interaction from the Swin Transformer network with deep local information integration from Convolutional Neural Networks (CNNs). Moreover, we introduce a deformable convolution-based attention mechanism module (D-CBAM) that adaptively adjusts receptive field size and shape, highlights important channels in feature maps automatically, and enhances the expressive ability of our network. Additionally, we incorporate a Feature Pyramid Attention (FPA) module during the advanced coding stage for multi-scale feature learning to improve segmentation accuracy for small water bodies. To verify the effectiveness of our method, we chose the Yellow River Basin in China as the research area and used Sentinel-2 and Sentinel-1 satellite images as well as manually labelling samples to construct a dataset. On this dataset, our method achieves a 93.7% F1 score, which is a significant improvement compared with other methods. Finally, we use the proposed method to map the seasonal and permanent water bodies in the Yellow River Basin in 2021 and compare it with existing water bodies. The results show that our method has certain advantages in mapping large-scale water bodies, which not only ensures the overall integrity but also retains local details.

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“…Today, diverse standard CNN models have been widely employed for water body segmentation tasks. Several widely recognized and exceptional algorithms, such as SegNet [13], U-Net [14], RefineNet [15], PSPNet [16], Mask R-CNN [17], Deeplab series [18][19][20][21], DUPNet [22], CoANet [23], D-LinkNet [24], and PANet [25]. The advent of deep learning techniques has revolutionized the segmentation paradigm from traditional methods, significantly enhancing the accuracy and speed of water body segmentation.…”

Section: Introductionmentioning

confidence: 99%

DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images

Zhang,

Li,

Wang

et al. 2024

Remote Sensing

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The frequent occurrence of global flood disasters leads to millions of people falling into poverty each year, which poses immense pressure on governments and hinders social development. Therefore, providing more data support for flood disaster detection is of paramount importance. To facilitate the development of water body detection algorithms, we create the DaliWS dataset for water segmentation, which contains abundant pixel-level annotations, and consists of high spatial resolution SAR images collected from the GaoFen-3 (GF-3) satellite. For comprehensive analysis, extensive experiments are conducted on the DaliWS dataset to explore the performance of the state-of-the-art segmentation models, including FCN, SegNeXt, U-Net, and DeeplabV3+, and investigate the impact of different polarization modes on water segmentation. Additionally, to probe the generalization of our dataset, we further evaluate the models trained with the DaliWS dataset, on publicly available water segmentation datasets. Through detailed analysis of the experimental results, we establish a valuable benchmark and provide usage guidelines for future researchers working with the DaliWS dataset. The experimental results demonstrate the F1 scores of FCN, SegNeXt, U-Net, and DeeplabV3+ on the dual-polarization data of DaliWS dataset reach to 90.361%, 90.192%, 92.110%, and 91.199%, respectively, and these four models trained using the DaliWS dataset exhibit excellent generalization performance on the public dataset, which further confirms the research value of our dataset.

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DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

Cited by 10 publications

References 47 publications

A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

An Enhanced Dual-Stream Network Using Multi-Source Remote Sensing Imagery for Water Body Segmentation

DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images

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