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Hyperspectral Change Detection Based on Multiple Morphological Profiles
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AI
DOI：10.1109/TGRS.2021.3090802.png)
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摘要
En 中文
With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.
Keyword:
Feature extraction
Hyperspectral imaging
Vegetation
Principal component analysis
Time series analysis
Standards
Spectral analysis
Change detection
guided filtering
hyperspectral image (HSI)
morphological attribute profiles (APs)
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
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RADIOLOGY
RADIOLOGYIF15.2
PREAI
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