Abstract
In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR). However, most of the existing SR methods employ bicubic downsampling of high-resolution (HR) images to obtain low-resolution (LR) images and use the obtained LR and HR images as training pairs. This supervised method that uses ideal kernel (bicubic) downsampled images to train the network will significantly degrade performance when used in realistic LR remote sensing images, usually resulting in blurry images. The main reason is that the degradation process of real remote sensing images is more complicated. The training data cannot reflect the SR problem of real remote sensing images. Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR). It does not require pre-training on a dataset, only utilizes the internal information reproducibility of a single image, and uses the lower-resolution image downsampled from the input image to train the cross-dimension attention network (CDAN). The cross-dimension attention module (CDAM) selectively captures more useful internal duplicate information by modeling the interdependence of channel and spatial features and jointly learning their weights. The proposed CASSISR adapts well to real remote sensing image SR tasks. A large number of experiments show that CASSISR has achieved superior performance to current state-of-the-art methods.
Highlights
In the field of remote sensing, HR remote sensing images have rich textures and critical information
Because the real-world remote sensing dataset has no real images of the ground truth for reference in the testing stage, we only show the qualitative results of visual comparison
×2 and ×4 between CASSISR and convolutional neural networks (CNN)-based SR methods on RSSCN7-blur, RSC11-blur, WHU-RS19-blur, UC-Merced-blur, AID-blur, and NWPU45-blur datasets, respectively. It can be seen from the above visualization results that for the ‘non-ideal’ remote sensing dataset, the results of the CNN-based SR methods are fuzzy
Summary
In the field of remote sensing, HR remote sensing images have rich textures and critical information. They play an important role in remote sensing image analysis tasks such as fine-grained classification [1,2], target recognition [3,4], target tracking [5,6] and land monitoring [7]. Image SR is the process of restoring an HR image from a given LR image. It is a very ill-posed process because multiple HR solutions are mapped to one LR input. Many image SR methods have been proposed to solve this ill-posed problem, including early interpolation-based methods [8], reconstruction-based methods [9], and recent learningbased methods [10,11,12,13]
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