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How to solve multi-category image recognition and meet a certain accuracy is a key issue in the research of high-resolution remote sensing images, and it is of great significance. This article mainly studies artificial neural network in the classification of multi-source remote sensing images. This paper improves the efficiency and accuracy of image segmentation by studying the principle and implementation process of image segmentation algorithm from the two aspects of initial segmentation and region merging; secondly, it studies the method of object feature quantization and the image of different object features on the classification results; and finally, it selects BP neural network. The network classification method classifies the image objects and realizes the extraction and classification of high-resolution remote sensing images. Experiments in this paper show that for multi-source remote sensing image data, the overall accuracy of the two parallel classification algorithms is very similar, and both are close to 85%, which has a good classification effect. When performing large-scale image classification, the terrain types in the image will be more complicated. In this case, the extraction accuracy relative to the artificial neural network classification method will decrease, and the classification time will also become longer. This paper proves through experiments that the classification method of multi-source remote sensing images based on artificial neural network is feasible and has certain advantages. |
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