Aiming at the inaccuracy of the methods based on ground textures and space features in detecting new ground buildings, a novel Change Detection model based on Generative Adversarial Networks (CDGAN) was proposed. Firstly, a traditional image segmentation network (U-net) was improved by Focal loss function, and it was used as the Generator (G) of the model to generate the segmentation results of remote sensing images. Then, a convolutional neutral network with 16 layers (VGG-net) was designed as the Discriminator (D), which was used for discriminating the generated results and the Ground Truth (GT) results. Finally, the Generator and Discriminator were trained in an adversarial way to get a Generator with segmentation capability. The experimental results show that, the detection accuracy of CDGAN reaches 92%, and the IU (Intersection over Union) value of the model is 3.7 percentage points higher than that of the traditional U-net model, which proves that the proposed model effectively improves the detection accuracy of new ground buildings in remote sensing images.

Support Vector Machine (SVM) is a widely used classification technique. But the scalability of SVM to handle large data sets still needs much of exploration. Core Vector Machine (CVM) is a technique for scaling up a two class SVM to handle large data sets. However, it is computationally infeasible to use CVM to deal with the data set with mass Support Vectors (SV), as its training time is related to the number of SV. In this paper, a two-stage training algorithm combining CVM with SVM (CCS) was proposed. It first employed Minimum Enclosing Ball (MEB) based CVM algorithm to determine the potential core vectors, and then used labeling method to rapidly reconstruct training set, which aim is to reduce the scale of training set. After obtaining new training samples, SVM was adopted to deal with them. The experimental results indicate that the proposed approach can reduce the training time by 30% without losing the classification accuracy, and it is an efficient method for handling large-scale classification.