Remote sensing images are essential in many fields, such as land cover classification and building extraction. The huge difference between the directly acquired remote sensing images and the actual scene, due to the complex degradation process and hardware limitations, seriously affects the performance achieved by the same classification or segmentation model. Therefore, using super-resolution (SR) algorithms to improve image quality and achieve better results is an effective method. However, current SR methods only focus on the similarity of pixel values between SR and high-resolution (HR) images without considering perceptual similarities, which usually leads to the problem of oversmoothed and blurred edge details. Moreover, there is little attention to human visual habits and machine vision applications for remote sensing images. In this work, we propose the context aware edge-enhanced generative adversarial network (CEEGAN) SR framework to reconstruct visually pleasing images that can be practically applied in actual scenarios. In the generator of CEEGAN, we build an edge feature enhanced module (EFEM) to enhance the edges by combining the edge features with context information. Edge restoration block is designed to fuse multiscale edge features enhanced by EFEM and reconstruct a refined edge map. Furthermore, we designed an edge loss function to constrain the generated SR and HR similarity at the edge domain. Experimental results show that our proposed method can obtain SR images with a better reconstruction performance. Meanwhile, CEEGAN can achieve the best results on classification and semantic segmentation datasets for machine vision applications.

Multispectral (MS) pansharpening can improve the spatial resolution of MS images by fusing panchromatic (PAN) images, which have important applications in the fields of smart agriculture and environmental monitoring. However, existing supervised algorithms treat the original MS images as ground truth and generate training data under Wald’s protocol, resulting in a gap between the learned degradation process of the model and reality. This leads to the model having poor generalization and impractical. Unsupervised pansharpening methods often struggle to fully explore the rich information contained in images, leading to suboptimal pansharpening outcomes. In this work, we propose an unsupervised pansharpening algorithm based on double-cycle consistency that can learn directly from the original MS images without relying on artificially simulated degradation processes. Specifically, the network with cross-domain correlation information interaction is developed to achieve a deep fusion of spatial and spectral features. To address the inaccurate degradation mechanism representation of MS images, a spatial information extraction module based on scale invariance is developed to achieve an accurate representation. Meanwhile, double-cycle consistency loss is proposed to reduce the information loss caused by simulated degradation during the cycle process. Experimental results show that this method outperforms existing unsupervised pansharpening methods in both quantitative and qualitative evaluation of full-resolution images.

Existing Super-Resolution (SR) methods are typically trained using bicubic degradation simulations, resulting in unsatisfactory results when applied to remote sensing images that contain a wide variety of object shapes and sizes. The insufficient learning approach reduces the focus of models on critical object regions within the images. As a result, their practical performance is significantly hindered, especially in real-world applications where accuracy in object reconstruction is crucial. In this work, we propose a general learning strategy for SR models based on expert knowledge supervision, named EKS-SR, which can incorporate a few coarse-grained semantic information derived from high-level visual tasks into the SR reconstruction process. It utilizes prior information from three perspectives: regional constraints, feature constraints, and attributive constraints, to guide the model to focus more on the object regions within the images. By integrating these expert knowledge-driven constraints, EKS-SR can enhance the model’s ability to accurately reconstruct object regions and capture the key information needed for practical applications. Importantly, this improvement does not increase the inference time and does not require full annotation of the large-scale datasets, but only a few labels, making EKS-SR both efficient and effective. Experimental results demonstrate that the proposed method can achieve improvements in both reconstruction quality and machine vision analysis performance.