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[Purpose]To take the advantages of a variety of remote sensing data, the application of remote sensing image classification is a very important choice.Remote sensing image classification is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, the amount of various remote sensing data obtained is getting larger and larger,the issue of how to fuse remote sensing image quickly and accurately and of getting useful information is becoming more and more urgent especially in some remote sensing applications such as disaster monitoring, prevention and relief, etc.In this paper, in order to fuse remote sensing image quickly and accurately, a parallel classification algorithm of multi-spectral image and panchromatic image based on wavelet transform is proposed.[Methods]In the method, based on parallel computing, the low-frequency components of wavelet decomposition are fused with the classification rule based on the feature matching, and the high-frequency components of wavelet decomposition are fused with the classification rule based on the sub-region variance. Then the low-frequency components and the high-frequency components after classification are processed with the inverse wavelet transform, and the fused image is obtained. According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification.[ Results] The experiment results show that the proposed method can get better classification results and faster computing speed for multi-spectral image and panchromatic image. Originality. In the proposed classification algorithm of multi-spectral image and panchromatic image, wavelet transform and different proper classification rules for low-frequency components and high frequency components of wavelet decomposition are used. To get a high speed, parallel computing is also taken in some complex parts of the proposed classification algorithm. Thus, better classification results and faster computing speed are obtained. [Conclusions] Practical value. The experiments have proved that the proposed algorithm can quickly get good classification results for remote sensing images, and it is useful in remote sensing applications in some aspects such as disaster monitoring, prevention and relief, Hybrid parallel Computing method for tasks and data (Pixels) etc.
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