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A new method for remote sensing image fusion based on multi-scale sparse decomposition |
| XU Jindong, NI Mengying, TONG Xiangrong, ZHANG Yanjie, ZHENG Qiang |
| School of Computer and Control Engineering, Yantai University, Yantai 264005, China |
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Abstract To achieve better effect of remote sensing image fusion, the authors put forward a multi-scale sparse image decomposition method based on morphological component analysis (MCA). It combines curvelet transform basis and local discrete cosine transform(DCT)basis to form the decomposition dictionary and controls the entries of the dictionary so as to decompose the image into texture component and cartoon component. From the aspect of the amount of information, a remote sensing image(RSI)fusion method based on multi-scale sparse decomposition was proposed. By using sparse decomposition, the effective scale texture component of high resolution RSI and cartoon component of multi-spectral RSI were selected to be fused together. Compared with the classical fusion methods, the proposed fusion method gets higher spatial resolution and lower spectral distortion with a little computation load. Compared with sparse reconstruction fusion method, it achieves a higher algorithm speed and a better fusion result. Therefore,the proposed image fusion method based on multi-scale sparse decomposition has certain application value.
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| Keywords
winter wheat
remote sensing
NDVI
time window
northwest Shandong Province
phenology
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Issue Date: 15 August 2017
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2017,
Vol. 29
