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Deep learning, especially convolutional neural network (CNN), has been widely applied in remote sensing scene classification in recent years. However, such approaches tend to extract features from the whole image rather than discriminative regions. This article proposes a self-attention network model with joint loss that effectively reduce the interference of complex backgrounds and the impact of intra-class diversity in remote sensing image scene classification. In this model, self-attention mechanism is integrated into ResNet18 to extract discriminative features of images, which makes CNN focus on the most salient region of each image and suppress the interference of the irrelevant information. Moreover, for reducing the influence of intra-class diversity on scene classification, a joint loss function combining center loss with cross-entropy loss is proposed to further improve the accuracy of complex scene classification. Experiments carried out on AID, NWPU-NESISC45 and UC Merced datasets show that the overall accuracy of the proposed model is higher than that of most current competitive remote sensing image scene classification methods. It also performs well in the case of fewer data samples or complex backgrounds.

In remote sensing, it is quite necessary to fuse spectral information of low-resolution multispectral (LRMS) images and spatial information of panchromatic (PAN) images for obtaining high-resolution multispectral (HRMS) images. In this paper, an effective fusion method integrating multispectral (MS) image super-resolution and convolutional sparse representation (CSR) fusion is proposed to make full use of the spatial information of remote sensing images. First, for enhancing the spatial information of LRMS images with suitable sizes, a fast iterative image super-resolution algorithm based on the learned iterative shrinkage and thresholding algorithm (LISTA) is exploited in the first stage. It employs a feed-forward neural network to simplify the solution of sparse coefficients in the process of super-resolution. In the fusion stage, we propose a CSR-based image fusion framework, in which each MS super-resolution image and PAN image is decomposed into a basic layer and a detail layer, then we fuse the basic layers and the detail layers of the images, respectively. This hierarchical fusion strategy guarantees great performance in detail preservation. The experimental results on QuickBird, WorldView-2, and Landsat ETM+ datasets demonstrate that the proposed method outperforms other methods in terms of both objective evaluation and visual effect.