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- Title
DIMENSION EXTRACTION OF REMOTE SENSING IMAGES IN TOPOGRAPHIC SURVEYING BASED ON NONLINEAR FEATURE ALGORITHM.
- Authors
YANI WANG; YINPENG ZHOU; BO WANG
- Abstract
In order to solve the problem of inaccurate image feature extraction caused by traditional extraction methods, this paper proposes a remote sensing image size extraction method based on nonlinear multi feature fusion for topographic maps. In this paper, SVM and DS evidence theory are combined to extract image features and classify pre processed remote sensing images. Based on the classification results, basic probability distributions are constructed, and a DS fusion algorithm using matrix analysis is introduced to simplify the complexity of decision level fusion algorithms; We use a multi feature fusion algorithm based on feature proximity, using the proximity vector formed by the attraction between the feature vector and the original graphics pattern as the fusion feature to complete the extraction of remote sensing image features. The simulation results show that after using this method, its soft threshold classifier outputs 0.9865, 0.9965, 0.7852, 0.9921, 0.9847, 0.6879, -0.5898, -0.5678, -0.6897, -0.4785. The algorithm in this paper can distinguish the shape features of terrain images well, and can extract the features of terrain images more accurately, which has strong feasibility.
- Subjects
FEATURE extraction; REMOTE sensing; TOPOGRAPHIC maps; SURVEYING (Engineering); DISTRIBUTION (Probability theory)
- Publication
Scalable Computing: Practice & Experience, 2024, Vol 25, Issue 5, p4246
- ISSN
1895-1767
- Publication type
Article
- DOI
10.12694/scpe.v25i5.3192