Research on Remote Sensing Image Target Recognition Based on Deep Convolution Neural Network

摘要

Target recognition is an important application in the time of high-resolution remote sensing images. However, the traditional target recognition method has the characteristics of artificial design, and the generalization ability is not strong, which makes it difficult to meet the requirement of the current mass data. Therefore, it is urgent to explore new methods for feature extraction and target recognition and location in remote sensing images. Convolutional neural network in deep learning can extract representative and discriminative multi-level features of typical features from images, so it can be used for multi-target recognition of remote sensing big data in complex scenes. In this study, NWPU VHR-10 data was selected, 50% was used for training, and the remainder was used for verification. The target recognition effects of two kinds of convolutional neural network models, Faster R-CNN and SSD, were studied and compared, and the mean average precision (mAP) was used for evaluation. The evaluation results show that the Faster R-CNN has three categories with an accuracy of more than 80%, and the SSD has seven categories with an accuracy of more than 80%, all of which show good results. The SSD model is particularly prominent in running time and recognition results, which proves convolutional neural networks have broad application prospects in the target recognition of remote sensing image data.