Removal of Optically Thick Clouds From High-Resolution Satellite Imagery Using Dictionary Group Learning and Interdictionary Nonlocal Joint Sparse Coding
| Autor: | Wenbo Li, Ying Li, Chunhua Shen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Synthetic aperture radar
Atmospheric Science 010504 meteorology & atmospheric sciences Computer science business.industry 0211 other engineering and technologies Pattern recognition 02 engineering and technology Iterative reconstruction 01 natural sciences Composite image filter Image (mathematics) Computer vision Artificial intelligence Computers in Earth Sciences Joint (audio engineering) Adaptive optics Representation (mathematics) business Neural coding 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10:1870-1882 |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2017.2655101 |
| Popis: | In this paper, we propose a method for cloud removal in a cloud-contaminated high-resolution (HR) optical satellite image with two kinds of auxiliary images of different types: a low-resolution (LR) optical satellite composite image and a synthetic aperture radar (SAR) image. In the proposed method, we assume that cloud-contaminated and cloud-free regions have been detected accurately, then dictionary group learning (DGL) is used to establish structure correspondences between HR, LR, and SAR data from cloud-free patches, while interdictionary nonlocal joint sparse coding (INJSC) is used to estimate the universal representation coefficients of patches contaminated by clouds, and finally, cloud-contaminated HR patches can be reconstructed with their universal coefficients and the HR dictionary learned from DGL process. In this way, the missing information in the cloud-contaminated HR image can be reconstructed patch by patch. The proposed method is tested on a series of experiments on both simulated and real data. Experimental results show that both DGL and INJSC are beneficial to better reconstructing the missing information. This method is also compared against our previous work on the same topic, which adopted dictionary pair learning (DPL) and sparse coding (SC) to recover the missing information and achieved state-of-the-art performance at that time. The comparison shows that the method proposed in this paper significantly outperforms the previous one. |
| Databáze: | OpenAIRE |
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