Hierarchical spectrum recognition based on WP-GS image fusion, derivative transformation, object oriented segmentation and multispectral SAM —Take EO-1/Hyperion images of Lanzhou and Yuyang as an example
Received:July 10, 2018  Revised:January 15, 2019  download
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Author NameAffiliationE-mail
LIU Wei Xizang Key Laboratory of Optical Information Processing and Visualization Technology remote2009@126.com 
Abstract:【Objective】This paper points out hierarchical spectrum recognition in the aspect of hyper-spectral image classification using Hyperion images of Lanzhou and Yuyang in China, by the comparison study of SVM supervised classification.【Method】There are 4 problems for hyper-spectral image classification: spectral information fidelity image fusion, extraction of sensitive band, removal of "salt and pepper effect", avoid misclassification for "same body with different spectrum" phenomenon. Hierarchical spectrum recognition puts forward 4 methods for solving these issues, which are WP-GS image fusion, derivative transformation, object oriented segmentation with 4 scales, multispectral SAM. Hierarchical spectrum recognition can identify 9 kinds land type exactly, based on sensitive bands extracted from derivative transformation image. Visual examination and quantifiable evaluation have been executed to verify authenticity. By contrast, SVM supervised classification was tested, with Gram-Schmidt Spectral sharpening/ Savitzky-Golay convolution filtering/PCA transformation.【Result】Using the method of hierarchical spectrum recognition proposed in this paper, the above 4 problems for hyper-spectral image classification were solved, and good classification results was achieved, with overall classification accuracy and kappa coefficient as 87.13%, 0.8303 in Lanzhou, 87.13%、0.8303 in Yuyang. Growth of 15.97% in Lanzhou and 87.13% in Yuyang for overall classification accuracy, as well as 12.44% in Lanzhou and 87.13% in Yuyang for kappa coefficient were achieved compared with SVM supervised classification.【Conclusion】Compared with SVM supervised classification, hierarchical spectrum recognition proposed in this paper can provide more accurate recognition results for Hyperion images classification.
keywords:satellite-borne hyper-spectral image  derivative spectrum feature  sensitive bands  same body with different spectrum  multi-scale  multispectral SAM
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Copyright:《Journal of Infrared And Millimeter Waves》