Subpixel mapping of hyperspectral images based on collaborative representation

Subpixel mapping with a low resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image with high spatial resolution is not always available in practice. In this paper, to extract spatial information without auxiliary image, the upscaled low resolution hyperspectral image is classified using collaborative representation-based classifier. Another subpixel scale classification map is available by the combination of collaborative representation-based classification, spectral unmixing and subpixel spatial attraction model. To achieve better classification performance, decision fusion is employed to elect approximate class label from these two initial classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed approach is more promising in extracting and utilizing spatial information compared with some state-of-the-art subpixel mapping approaches.

[1]  Qian Du,et al.  Joint Within-Class Collaborative Representation for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Jon Atli Benediktsson,et al.  Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution , 2011, IEEE Journal of Selected Topics in Signal Processing.

[3]  James E. Fowler,et al.  Nearest Regularized Subspace for Hyperspectral Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

[6]  Qian Du,et al.  Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification , 2014, IEEE Geoscience and Remote Sensing Letters.

[7]  Paul Scheunders,et al.  Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.