Open AccessChange Detection Based on Feature Optimization in High Resolution Optical Image
Author(s) -
Haokai Pang,
Xuezhi Yang,
Jun Wang,
Zhicheng Dong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012067
Subject(s) - artificial intelligence , pattern recognition (psychology) , feature (linguistics) , computer science , computer vision , redundancy (engineering) , feature detection (computer vision) , feature vector , image (mathematics) , image processing , philosophy , linguistics , operating system
Object-oriented change detection (CD) method can make full use of feature information of high resolution optical images. However, the feature information is redundancy in Object-oriented CD of high resolution optical image due to the fact that the image has multiple bands. Therefore, feature optimization is necessary to object-oriented CD. Aiming at the problem, a novel CD method based on feature optimization which combines improved locally linear embedding (ILLE) algorithm and object-oriented technology is proposed. Firstly, two temporal images are inverted into objects using multi-scale segmentation algorithm. Secondly, the spectral and texture features of the objects are extracted to construct the novel feature change vector. Thirdly, the improved LLE algorithm, which introduces the Geodesic distance metric, is designed to optimize the feature change vector. Finally, the CD result is obtained by FCM algorithm. Experiments construct on the real GF-1 images, and the results confirm the effectiveness of the proposed method.