Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification

Abstract

With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a better prediction performance. The lack of large-scale tagged remote sensing scene images has become the primary bottleneck in scene classification. To deal with this issue, a novel scene classification method using self-supervised gated self-attention generative adversarial networks (SGSAGANs) with similarity loss is proposed. Specifically, the gated self-attention module is first introduced into GANs to focus on key scene areas and filter useless information for strengthening feature representations. Then, the pyramidal convolution (PyConv) block is introduced into the residual block of the discriminator to capture different levels of details in the image using different types of filters with varying sizes and depths for enhancing the feature representations of the discriminator. Additionally, a novel similarity loss item is integrated into the discriminator to leverage self-supervised learning. Besides, spectral normalization is introduced into both the generative network and discriminative network to stabilize training and enhance feature representations. The architecture of multi-level feature fusion is integrated into the discriminative network to achieve more discriminant representation. Experimental results on the AID and NWPU-RESISC45 datasets show that the proposed method achieves the best performance compared to the existing unsupervised classification methods.

Innovative Hyperspectral Image Classification Approach Using Optimized CNN and ELM

“…Gu et al [43] presented a pseudo labeled sample generation method. Guo et al [44] presented a novel self-supervised gated self-attention GAN. Li et al [45] presented a novel locally preserving deep cross embedded classification network.…”

Multi-View HRRP Generation With Aspect-Directed Attention GAN

“…In this paper, the multi-view HRRP generation task is addressed by the deep generative networks (DGNs). Recently, DGNs, especially generative adversarial networks (GANs), have proven the ability of generating new realistic-looking samples [30][31][32][33][34][35][36]. In radar, GAN has been applied in various applications, such as meteorological radar extrapolation [37], synthetic aperture radar (SAR) image enhancement [38] and optical and SAR image matching [39].…”

Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

“…However, labeling remote sensing images is often costly. Unsupervised scene classification methods can learn directly from a large number of unlabeled samples, but their classification accuracy is difficult to meet practical applications because they cannot make full use of labels [3]. Semi-supervised scene classification is a combination of supervised and unsupervised, which can learn from a small number of labeled samples and a large number of unlabeled samples to obtain satisfactory classification accuracy [4].…”