Improving GAN-based feature extraction for hyperspectral images classification

With the recent development of machine learning theory, many deep learning based methods have been proposed to extract features from hyperspectral sensed images (HSIs) and superior performance has been achieved. Although the data of HSIs is characterized by its high volume as well as with rich spectral and spatial information, the number of the labeled samples is always very limited. But many deep learning methods, especially the supervised approaches, often require numerous labeled samples to extract the effective features, and a prolonged training procedure is inevitable. To expand the set of the labeling samples, a huge amount of manpower and material resources are required, which may make the application unaffordable. In order to alleviate the dependence on HSIs' labeled samples, in this paper an unsupervised spatial-spectral feature extraction method is proposed. Combining the two-dimensional convolutional neural network (2DCNN) with generative adversarial networks (GANs), the requirement of the labeled samples is reduced for the training of a learning network, and the spatial-spectral characteristic of the HSIs is utilized at the same time. Moreover, the principal component analysis (PCA) is introduced as a preprocess for the high-dimensional hyperspectral data, which not only removes redundant information, but also fits the proposed feature extraction model to hyperspectral data with different dimensions. To improve the generalization capability of the proposed model, a gradient penalty term is further investigated in the objective function of the GAN' s discriminator, which can optimize the discriminator's convergence and improve the model's overall generalization capability. Finally, a support vector machine is applied to classify the extracted features. Experimental results on three real data sets show that the proposed method can extract features from the hyperspectral data effectively. Through the GANs-based feature extraction and the specialized regularization, the proposed method achieves a good level of generalization capability, in which a model validated on a single dataset can be extended to different datasets while a satisfactory performance still remained. (C) 2021 SPIE and IS&T