Adaptive Sparse Subpixel Mapping With a Total Variation Model for Remote Sensing Imagery
- 7 January 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 54 (5), 2855-2872
- https://doi.org/10.1109/tgrs.2015.2506612
Abstract
Subpixel mapping, which is a promising technique based on the assumption of spatial dependence, enhances the spatial resolution of images by dividing a mixed pixel into several subpixels and assigning each subpixel to a single land-cover class. The traditional subpixel mapping methods usually utilize the fractional abundance images obtained by a spectral unmixing technique as input and consider the spatial correlation information among pixels and subpixels. However, most of these algorithms treat subpixels separately and locally while ignoring the rationality of global patterns. In this paper, a novel subpixel mapping model based on sparse representation theory, namely, adaptive sparse subpixel mapping with a total variation model (ASSM-TV), is proposed to explore the possible spatial distribution patterns of subpixels by considering these subpixels as an integral patch. In this way, the proposed method can obtain the optimal subpixel mapping result by determining the most appropriate subpixel spatial pattern. However, the number of possible spatial configurations of subpixels can increase sharply with large-scale factors, and therefore, in ASSM-TV, the subpixel mapping is considered as a sparse representation problem. A preconstructed discrete cosine transform dictionary, which consists of piecewise smooth subpixel patches and textured patches, is utilized to express the original subpixel mapping observation in a sparse representation pattern. The total variation prior model is designed as a spatial regularization constraint to characterize the relationship between a subpixel and its neighboring subpixels. In addition, a joint maximum a posteriori model is proposed to adaptively select the regularization parameters. Compared with the other traditional and state-of-the-art subpixel mapping approaches, the experimental results using a simulated image, three synthetic hyperspectral remote sensing images, and two real remote sensing images demonstrate that the proposed algorithm can obtain better results, in both visual and quantitative evaluations.Keywords
Funding Information
- National Natural Science Foundation of China (41371344)
- State Key Laboratory of Earth Surface Processes and Resource Ecology (2015-KF-02)
- Open Research Found Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services
- Natural Science Foundation of Hubei Province (2015CFA002)
This publication has 47 references indexed in Scilit:
- On the Performance of Manhattan Nonnegative Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems, 2015
- Multi-View Intact Space LearningIEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
- Large-Margin Multi-ViewInformation BottleneckIEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
- Sub-Pixel Mapping Based on a MAP Model With Multiple Shifted Hyperspectral ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
- Total Variation Spatial Regularization for Sparse Hyperspectral UnmixingIEEE Transactions on Geoscience and Remote Sensing, 2012
- Sparse and Redundant RepresentationsPublished by Springer Science and Business Media LLC ,2010
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?IEEE Transactions on Information Theory, 2006
- Stable signal recovery from incomplete and inaccurate measurementsCommunications on Pure and Applied Mathematics, 2006
- A Non-Local Algorithm for Image DenoisingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 1992