Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

Liu, Yu; Yang, Yun Jung; Zhao, Quanhua

doi:10.3390/rs12244014

Cited by 2 publications

(3 citation statements)

References 25 publications

(30 reference statements)

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“…Three different matrixes Q, K, and V, generated by trainable 1 × 1 convolutions W q , W k and W v times input x, denote queries, keys, and values. In formula (7), attention weights of each self-attention head are generated by Q • K first. Then attention maps are normalized by √ d i and softmax function to obtain the attention scores that contain global contextual information.…”

Section: Mhsamentioning

confidence: 99%

“…With the dramatic increase in spaceborne SAR images, automatic interpretation of SAR images is a promising technology in the earth observation and surveillance [1][2][3][4]. As a vital factor for urban ecosystem, urban water is of great importance to urban ecological landscapes, urban development, and flood control [5][6][7]. Accurate and effective urban water extraction is a critical task in automatic interpretation of SAR images.…”

Section: Introductionmentioning

confidence: 99%

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HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Song

Huang

et al. 2022

Electronics

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Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

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Section: Mhsamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Song

Huang

et al. 2022

Electronics

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show abstract

“…However, to our knowledge, a large number of papers pay more attention to the exploration for large lakes [14,[17][18][19][20][21], rivers [15,16], and the reservoirs [22,23], but few studies refer to the identification of water bodies in urban areas [8], which mainly includes urban rivers, ponds, small lakes, and fishing pools [1]. Li et al [24] described a method of extracting complex urban rivers, but different subimage blocks had to be manually adjusted and selected for certain operations; therefore, there was a lack of automation. Mason et al [25] used SAR images and LiDAR data to collect urban flood information semiautomatically, and only 76% of the water was correctly detected.…”

Section: Introductionmentioning

confidence: 99%

Water Extraction in SAR Images Using Features Analysis and Dual-Threshold Graph Cut Model

Bao

Yao

2021

Remote Sensing

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Timely identifying and detecting water bodies from SAR images are significant for flood monitoring and water resources management. In recent decades, deep learning has been applied to water extraction but is subject to the large difficulty of acquiring SAR dataset of various water bodies types, as well as heavy labeling work. In addition, the traditional methods mostly occur over the large, open lakes and rivers, rarely focusing on complex areas such as the urban water, and cannot automatically acquire the classification threshold. To address these issues, a novel water extraction method is proposed with high accuracy in this paper. Firstly, a multiscale feature extraction using a Gabor filter is conducted to reduce the noise and roughly identify water feature. Secondly, we apply the Otsu algorithm as well as a voting strategy to initially extract the homogeneous regions and for subsequent Gaussian mixture model (GMM). Finally, the dual threshold is obtained from the fitted Gaussian distribution of water and non-water, which is integrated into the graph cut model to redefine the weights of the edges, then constructing the energy function of the water map. The dual-threshold graph cut (DTGC) model precisely pinpoints the water location by minimizing the energy function. To verify the efficiency and robustness, our method and comparison methods, including the IGC method and IACM method, are tested on six different types of water bodies, by performing the accuracy assessment via comparing outcomes with the manually labeled ground truth. The qualitative and quantitative results show that the overall accuracy of our method for the whole dataset all surpasses 99%, along with an obvious improvement of the Kappa, F1-score, and IoU indicators. Therefore, DTGC method has the absolute advantage of automatically capturing water maps in different scenes of SAR images without specific prior knowledge and can also determine the optimal threshold range.

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Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

Cited by 2 publications

References 25 publications

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Water Extraction in SAR Images Using Features Analysis and Dual-Threshold Graph Cut Model

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