Pattern recognition (psychology)

Image (mathematics)

Data mining

Political science

Politics

Computer science

Philosophy

Representation (politics)

Computer vision

Relation (database)

Linguistics

Deep learning

Feature (linguistics)

Artificial intelligence

Similarity (geometry)

Sparse approximation

Autoencoder

Image resolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Remote sensing image super-resolution (SR) refers to a technique improving the spatial resolution, which in turn benefits to the subsequent image interpretation, e.g., target recognition, classification, and change detection. In popular sparse representation-based methods, due to the complex imaging conditions and unknown degradation process, the sparse coefficients of low-resolution (LR) observed images are hardly consistent with the real high-resolution (HR) counterparts, which leads to unsatisfactory SR results. To address this problem, a novel coupled sparse autoencoder (CSAE) is proposed in this paper to effectively learn the mapping relation between the LR and HR images. Specifically, the LR and HR images are first represented by a set of sparse coefficients, and then, a CSAE is established to learn the mapping relation between them. Since the proposed method leverages the feature representation ability of both sparse decomposition and CSAE, the mapping relation between the LR and HR images can be accur...

Remote Sensing Image Super-Resolution Using Sparse Representation and Coupled Sparse Autoencoder