Image Generation Based On Texture Guided Vae-Agan For Regions Of Interest Detection In Remote Sensing Images

Deep learning has shown great strength in regions of interest (ROIs) detection for remote sensing images (RSIs). However, for most of RSIs, the unbalanced distribution of positive and negative samples greatly limits the performance of the deep learning-based methods. To cope with this issue, we propose a novel method based on texture guided variational autoencoder-attention wise generative adversarial network (VAE-AGAN) to augment the training data for ROI detection. First, to generate realistic texture details of RSIs, we propose a texture guidance block to embed texture prior information into encoder and decoder networks. Second, we introduce the channel and spatial-wise attention layers in the discriminator construct to adaptively recalibrate the varying importance of different channels and spatial regions of input RSIs. Finally, we apply the RSI dataset balanced by our proposal to the weakly supervised ROI detection method. Experimental results demonstrate that the proposal can not only improve the performance of ROI detection, but also outperform other competing augmentation methods.