Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data

Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2018-01, Vol.2018 (2018), p.1-9
Main Authors: Zhang, Zhiqiang, Ma, Bin, Gao, Feng, Zhang, Zhenxing
Format: Article
Language:English
Subjects:
Detection
Earth science
Earth surface
Efficiency
Feature extraction
Hardware reviews
Pattern recognition
Principal components analysis
Remote sensing
Spatial distribution
Surface layers
Texture
Wavelet transforms
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Summary:Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spatial approach is designed to extract multiband remote sensing data, utilizing principle component analysis conversion to compress the multispectral information, and it establishes the gray level difference frequency spatial of principle components. In the end, the texture features are extracted using the gray level difference frequency spatial. To verify the effectiveness of this approach, several experiments are conducted and indicate that it could retain the coordination relationship among multispectral remote sensing data, and compared with the traditional single-band texture analysis method that is based on gray level co-occurrence matrix, the proposed approach has higher classification precision and efficiency.
ISSN:2090-0147
2090-0155
DOI:10.1155/2018/9684629