Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net
Published in IEEE Geoscience and Remote Sensing Letters, 2019
Recommended citation: Wenzhi Zhao, et al. (2019). "Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net." IEEE Geoscience and Remote Sensing Letters. 17(3). https://ieeexplore.ieee.org/abstract/document/8754768/
Hyperspectral image classification is a challenging task when a limited number of training samples are available. It is also known that the classification performance highly depends on the quality of the labeled samples. In this work, a cluster-based conditional generative adversarial net (CCGAN) is proposed as an effective solution to increase the size and quality of the training data set. The proposed method is able to automatically select the most representative initial samples with a subtractive clustering-based strategy, which keeps the diversity for sample generation.
Recommended citation: Wenzhi Zhao, et al. (2019). “Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net” IEEE Geoscience and Remote Sensing Letters. 12(5).