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Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion 被引量:4
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作者 Weiguo LI Nan JIANG Guangxiu GE 《Agricultural Science & Technology》 CAS 2014年第11期2035-2038,2040,共5页
Because of cloudy and rainy weather in south China, optical remote sens-ing images often can't be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model an... Because of cloudy and rainy weather in south China, optical remote sens-ing images often can't be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model (IBSM), the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods. 展开更多
关键词 Spectral characteristics data fusion sar Multi-spectral image Wavelet transform
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