Sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images

Abstract

High-resolution remote-sensing images are increasingly applied in land-use classification problems. Land-use scenes are often very complex and difficult to represent. Subsequently, the recognition of multiple land-cover classes is a continuing research question. We propose a classification framework based on a sparse coding-based correlaton (termed sparse correlaton) model to solve this challenge. Specifically, a general mapping strategy is presented to label visual words and generate sparse coding-based correlograms, which can exploit the spatial co-occurrences of visual words. A compact spatial representation without loss discrimination is achieved through adaptive vector quantization of correlogram in land-use scene classification. Moreover, instead of using K-means for visual word encoding in the original correlaton model, our proposed sparse correlaton model uses sparse coding to achieve lower reconstruction error. Experiments on a public ground truth image dataset of 21 land-use classes demonstrate that our sparse coding-based correlaton method can improve the performance of land-use scene classification and outperform many existing bag-of-visual-words-based methods.