Collaborative Optimization of Superpixel-Based Graph Cuts for Land Use Change Detection

To address the challenges of noise interference and boundary ambiguity in land-use change detection for highresolution remote sensing images, this study proposes a detection method based on superpixel-graph cut collaborative optimization. By fusing spectral differences between coregistered images fr...

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Bibliographic Details
Published in2025 IEEE 2nd International Conference on Deep Learning and Computer Vision (DLCV) pp. 1 - 9
Main Authors Tao, Tianyang, Li, Hui, Liu, Yu, Jia, Yongjun, Wang, Yidi
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.06.2025
Subjects
Accuracy
Change detection
Change detection algorithms
Collaboration
Feature extraction
Graph cut algorithm
Image segmentation
Noise
Object oriented modeling
Optimization
Remote sensing
SLIC
Soft sensors
superpixel
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DOI10.1109/DLCV65218.2025.11088819

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Summary:To address the challenges of noise interference and boundary ambiguity in land-use change detection for highresolution remote sensing images, this study proposes a detection method based on superpixel-graph cut collaborative optimization. By fusing spectral differences between coregistered images from two periods, superpixel units are generated using the SLIC algorithm. Multi-dimensional change descriptors are constructed by integrating spectral and LBP texture features, with change intensity quantified via cosine similarity. A graph-cut energy function is further designed to incorporate local consistency of superpixels and spatial continuity constraints, and the global optimal solution is achieved using the min-cut/max-flow algorithm. Experimental results demonstrate that the proposed method effectively eliminates "salt-and-pepper" noise while accurately identifying complex land cover boundaries, providing a highly robust technical solution for dynamic natural resource monitoring.
DOI:10.1109/DLCV65218.2025.11088819