Semantic segmentation of high-resolution remote sensing images has emerged as one of the foci of research in the remote sensing field, which can accurately identify objects on the ground and determine their localization. In contrast, the traditional deep learning-based semantic segmentation, on the other hand, requires a large amount of annotated data, which is unsuitable for high-resolution remote sensing tasks with limited resources. It is therefore important to build a semantic segmentation method for high-resolution remote sensing images. In this paper, it is proposed an improved U-Net model based on transfer learning to solve the semantic segmentation problem of high-resolution remote sensing images. The model is based on the symmetric encoder–decoder structure of U-Net. For the encoder, transfer learning is applied and VGG16 is used as the backbone of the feature extraction network, and in the decoder, after upsampling using bilinear interpolation, it is performed multiscale fusion with the feature maps of the corresponding layers of the encoder in turn and is finally obtained the predicted value of each pixel to achieve precise localization. To verify the efficacy of the proposed network, experiments are performed on the ISPRS Vaihingen dataset. The experiments show that the applied method has achieved high-quality semantic segmentation results on the high-resolution remote sensing dataset, and the MIoU is 1.70%, 2.20%, and 2.33% higher on the training, validation, and test sets, respectively, and the IoU is 4.26%, 6.89%, and 5.44% higher for the automotive category compared to the traditional U-Net.

Image matching is one of the basic problems in the field of machine vision research. In order to improve the accuracy of image feature point matching and enhance the anti-interference ability of the algorithm, for the shortcomings of traditional ORB algorithm, an image feature point matching algorithm based on ORB and RANSAC algorithm is proposed. The algorithm can filter out false matches on the basis of coarse matching results, and filter out exact matching points through the homography matrix. Experiments show that this method can effectively improve the accuracy of image matching and shorten the execution time, and it is very robust to image matching with different scales, ambiguities, brightness and rotation.

In the era of artificial intelligence and big data, semantic segmentation of images plays a vital role in various fields, such as people’s livelihoods and the military. The accuracy of semantic segmentation results directly affects the subsequent data analysis and intelligent applications. Presently, semantic segmentation of unmanned aerial vehicle (UAV) remote-sensing images is a research hotspot. Compared with manual segmentation and object-based segmentation methods, semantic segmentation methods based on deep learning are efficient and highly accurate segmentation methods. The author has seriously studied the implementation principle and process of the classical deep semantic segmentation model—the fully convolutional neural network (FCN), including convolution and pooling in the encoding stage, deconvolution and upsampling, etc., in the decoding stage. The author has applied the three structures (i.e., FCN-32s, FCN-16s, and FCN-8s) to the UAV remote sensing image dataset AeroScapes. And the results show that the accuracy of vegetation recognition is stable at about 94%. The accuracy of road recognition can reach up to more than 88%. The mean pixel accuracy rate of the whole test dataset is above 91%. Applying full convolution neural network to semantic segmentation of UAV remote sensing images can improve the efficiency and accuracy of semantic segmentation significantly.