Combination of SVD and GLCM in forest image recognition

Deshen Xia; Hua Li; Yong Qiu

doi:10.1117/12.262873

18 December 1996 Combination of SVD and GLCM in forest image recognition

Deshen Xia, Hua Li, Yong Qiu

Proceedings Volume 2907, Optics in Agriculture, Forestry, and Biological Processing II; (1996) https://doi.org/10.1117/12.262873
Event: Photonics East '96, 1996, Boston, MA, United States

Abstract

Feature extraction is the most fundamental and important problem in satellite forest image recognition problem, but how to extract the feature is an important step towards solving recognition problems. The forest image features commonly consist of visual feature, statistical feature, transform field feature, and algebraic feature. The paper uses SPOT remote sensing forest images as samples, SVD (singular value decomposition) and GLCM (gray-level co- occurrence matrix) as the methods of feature extraction, it also compares and analyzes these two methods. The results of comparison show that GLCM is more effective in describing the visual feature and SVD in describing the internal feature. Forest image statistical feature can describe the image macroscopic features, such as the texture feature shape feature, etc. But SVD can describe the image internal features. The SVD spectral features synthesize the features of image at the pixel level. The combination of the two methods can be more effective in the forest image recognition and classification. We use both SVD and GLCM to extract remote sensing forest image features, and by the combination of these two methods we have got a better recognition of the ground surface forest of remote sensing images than before. Our word shows that the forest recognition rate reaches up to 95%.

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Deshen Xia, Hua Li, and Yong Qiu "Combination of SVD and GLCM in forest image recognition", Proc. SPIE 2907, Optics in Agriculture, Forestry, and Biological Processing II, (18 December 1996); https://doi.org/10.1117/12.262873

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KEYWORDS

Remote sensing

Feature extraction

Image classification

Visualization

Detection and tracking algorithms

Earth observing sensors

Lithium

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