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Hyperspectral Change Detection Based on Multiple Morphological Profiles
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- 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.
- Subjects :
- Change detection algorithms
Computer science
business.industry
Detector
Hyperspectral imaging
Pattern recognition
Domain (software engineering)
Discriminative model
Feature (computer vision)
General Earth and Planetary Sciences
Artificial intelligence
Electrical and Electronic Engineering
business
Spatial analysis
Change detection
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi...........98c8c95331a3b8fe2131efbd33e6bf7f