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Remote sensing landform images usually contain a large amount of data, which is highly complex and diverse, making it difficult to capture texture information at different levels, thereby affecting recognition performance. Therefore, in order to improve the effectiveness of texture feature extraction and ensure recognition accuracy, a texture recognition method for remote sensing topographic images based on multi-scale semi coupled convolutional sparse encoding is proposed. Remove noise from remote sensing landform images, enhance the overall quality of remote sensing landform images, segment remote sensing landform images through watershed algorithm, and explore the differences in texture features of remote sensing landform images at different scales. Then, the gray level co-occurrence matrix (GLCM) is applied to obtain multi-scale texture features of remote sensing geomorphic images, and a semi coupled convolutional sparse encoding model is constructed to complete the learning of multi-scale texture feature extraction process and effective fusion of multi-scale texture features. An appropriate classifier - Naive Bayes classifier is selected and trained. Finally, based on this, a remote sensing terrain image texture recognition program is developed, and the results of terrain texture recognition can be obtained by executing the program. The test results show that the remote sensing landform image processing results obtained by the proposed method have high clarity and contrast, and the terrain texture feature extraction results are more complete and clear. The terrain texture recognition results are consistent with the actual results, fully confirming that the proposed method has better application effect.