Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data

Xie, Lei; Zhang, Hong; Wang, Chao; Chen, Fulong

doi:10.1016/j.jag.2016.02.012

Cited by 13 publications

(6 citation statements)

References 13 publications

(20 reference statements)

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“…Lei, X. [6] used fully polarized synthetic aperture radar data to identify types of urban water bodies, and used polarization features to eliminate misclassification of other ground objects and improve the accuracy of identification.…”

Section: Introductionmentioning

confidence: 99%

Vector water body type recognition integrating terrain features and spatial relationships

Jia,

Meng

2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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Section: Introductionmentioning

confidence: 99%

Vector water body type recognition integrating terrain features and spatial relationships

Jia,

Meng

2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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“…Precise classification not only optimizes the allocation of water resources, and enhances the efficiency of agricultural irrigation, but also plays a key role in urban planning. Yet, in the past, water-body type classification tasks primarily relied on traditional machine learning methods [24], [25]. While these methods have achieved certain research results in this field, there are still many problems that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…While these methods have achieved certain research results in this field, there are still many problems that need to be addressed. For instance, these methods often struggle to find an appropriate threshold and are significantly affected by speckle noise [25]. Additionally, they have difficulty fully utilizing the multifeature information of the images.…”

Section: Introductionmentioning

confidence: 99%

Water-Body Type Classification in Dual PolSAR Imagery Using a Two-Step Deep-Learning Method

Yuan,

Lu,

et al. 2024

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

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Water-body type classification plays a vital role in ecological conservation, water resource management, and urban planning. Accurate classification can aid decision-makers in understanding the functions of different water-body types, providing key information for urban planning and promoting harmony between human activities and the natural environment. Despite extensive research in the field of water-body segmentation, exploration in the water-body type classification community is not as widespread. Therefore, this article proposes a novel water-body type classification method based on a two-step deep learning model, decomposing water-body type classification into waterbody segmentation and water-body type identification. Especially, this method constructs a unique data strategy by organically integrating backscatter features, polarimetric features, and DEM features, providing the model with rich and comprehensive information. In the first step, the segmentation network uses the fused feature to extract all water-body from Synthetic Aperture Radar (SAR) images. Subsequently, the extracted water-body are combined with the input data, forming a multi-feature input for the identification network to distinguish between natural and artificial water-body. During this process, a swarm intelligence optimization algorithm is employed to explore the optimal hyperparameters of the network, including those of the segmentation and identification networks. Finally, the proposed method is assessed using extensive experiments on water-body segmentation tasks, water-body type identification tasks, and joint water-body type classification tasks. This article not only provides a new perspective in the field of water-body type classification but also demonstrates the immense potential of deep learning network hyperparameter optimization and feature fusion in solving such problems.

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“…However, they were not completely automatic and some of them required auxiliary information (e.g., DEM datasets). Xie et al [18] proposed a supervised water extraction method for urban area studies, which has combined both shape and polarimetric features from SAR images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network

Zhang

Chen

et al. 2019

Sensors

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The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

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Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data

Cited by 13 publications

References 13 publications

Vector water body type recognition integrating terrain features and spatial relationships

Vector water body type recognition integrating terrain features and spatial relationships

Water-Body Type Classification in Dual PolSAR Imagery Using a Two-Step Deep-Learning Method

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network

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