Abstract
In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.
Highlights
The term super-resolution (SR) imaging refers to the ill-posed problem of reconstructing a high-resolution image from a single low-resolution image
The performance of the proposed SR network is confirmed through various experiments, such as qualitative visual comparisons with other SR methods and quantitative numerical comparisons such as natural image quality evaluator (NIQE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)
A super-resolution method with adaptive image quality modification to improve the resolution of KOMPSAT-3 satellite images is proposed
Summary
The term super-resolution (SR) imaging refers to the ill-posed problem of reconstructing a high-resolution image from a single low-resolution image. Single-image SR techniques which are able to learn a mapping relationship from a low-resolution (LR) space to a high-resolution (HR). Space using deep learning have been recently studied. Dong et al [1] proposed a simple network architecture consisting of three convolutional layers, called super-resolution convolutional neural network (SRCNN), which outperformed previous machine learning-based SR approaches. Kim et al [2] proposed a very deep convolutional network based on VGG-net (VDSR), which was used in the ImageNet classification competition. VDSR demonstrated the capability to reconstruct HR images better than SRCNN by using 20 convolution layers in a very deep network.
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