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The classification of remote sensing images with high spatial resolution requires considerable training samples, but the process of sample making is slow and laborious. How to guarantee the accuracy of supervised classification under the condition of limited samples is an urgent problem to be solved in the field of supervised classification. For addressing this problem, we propose an improved conditional generative adversarial network with edge feature (ECGAN) to augment limited training data for the classification of remote sensing images with high spatial resolution in this article. On the basis of conditional generative adversarial network, feature factors of interclass boundaries and intraclass edges are added to networks, and an objective function with multiscale and multilevel features is constructed. The ISPRS potsdam and Vaihingen remote sensing datasets are regarded as examples. Results indicate that the high-resolution remote sensing images generated by using the network proposed in this article have abundant texture, accurate edges, and are highly similar to real images. The generated images are used to augment training samples, and an experiment for classifying high-resolution remote sensing images is conducted. The classification results of the proposed augmentation method perform better than that of the traditional sample augmentation method. We prove that ECGAN as a means of sample augmentation can effectively solves the problem that the classification effect is unideal when the supervised classification sample is insufficient.

ieee journal of selected topics in applied earth observations and remote sensing

ECGAN: An Improved Conditional Generative Adversarial Network With Edge Detection to Augment Limited Training Data for the Classification of Remote Sensing Images With High Spatial Resolution