Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image

Abstract

Recent studies have shown that deep learning methods provide useful tools for wetland classification. However, it is difficult to perform species-level classification with limited labeled samples. In this paper, we propose a semi-supervised method for wetland species classification by using a new efficient generative adversarial network (GAN) and Jilin-1 satellite image. The main contributions of this paper are twofold. First, the proposed method, namely ShuffleGAN, requires only a small number of labeled samples. ShuffleGAN is composed of two neural networks (i.e., generator and discriminator), which perform an adversarial game in the training phase and ShuffleNet units are added in both generator and discriminator to obtain speed-accuracy tradeoff. Second, ShuffleGAN can perform species-level wetland classification. In addition to distinguishing the wetland areas from non-wetlands, different tree species located in the wetland are also identified, thus providing a more detailed distribution of the wetland land-covers. Experiments are conducted on the Haizhu Lake wetland data acquired by the Jilin-1 satellite. Compared with existing GAN, the improvement in overall accuracy (OA) of the proposed ShuffleGAN is more than 2%. This work can not only deepen the application of deep learning in wetland classification but also promote the study of fine classification of wetland land-covers.