Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images

To achieve high segmentation quality, deep semantic segmentation neural networks (DSSNNs) need to be trained on diverse direction, location, and neighboring category combinations for each pixel in the input-output image patches. Achieving this goal requires a large sample set. However, in many practical application scenarios, a very large training sample set is too expensive to achieve, or insufficient remote sensing image data are available. These limitations directly affect the quality of the results of DSSNNs. To address the above-mentioned problem, this article proposes a conditional generative adversarial network (CGAN)-based training sample set improvement model (CGAN-TSIM) for the semantic segmentation of high-resolution remote sensing images. In CGAN-TSIM, the generator model of the CGAN can generate a sample image when a ground-truth image is an input as a "condition." A condition generation mechanism is designed to create ground-truth images, and these ground-truth conditions are used to drive the CGAN to generate samples containing more diverse object combinations, directions, and locations. These generated images can be added to the original training sample set to improve their spatial information diversity. Rather than simply relying on passively finding samples that contain diverse spatial information, CGAN-TSIM extracts high-level spatial information from the original training images and actively generates new sample images. Experiments show that the samples generated by CGAN-TSIM can improve the quality of the sample set. Compared with other traditional methods, CGAN-TSIM enables better classification accuracy.