Change detection of remote sensing images through DT-CWT and MRF

Fan, Kebin; Wang, Z.; Ouyang, Sida; Wang, H.; Shi, Shaoguang

doi:10.11834/jrs.20176251

Cited by 1 publication

(1 citation statement)

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“…In the 1990s, with the emergence of machine learning, researchers began to apply more sophisticated approaches for water detection, including artificial neural networks [7], support vector machines (SVM), decision trees, random forests [7][8][9], multi-kernel learning, and various hybrid methods such as spectral mixture analysis, fuzzy clustering analysis, and bio-inspired evolutionary algorithms. In the early 21st century, researchers shifted their focus towards object-based image analysis, introducing techniques like Markov random fields, conditional random fields, object-level change vector analysis, and other technologies [6,10,11]. Simultaneously, object category comparison method emerged, including hybrid method at the pixel and object levels.…”

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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