Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
A hyperspectral image is a collection of pixels that represent a given scene or object, where pixels represent the reflected solar radiation from the Earth’s surface in many narrow spectral bands. At each pixel, the spectral features form a vector whose elements correspond to the narrow bands covering visible to infrared regions of the spectrum.
The high spatial and spectral resolution of a hyperspectral image provides the potential for each pixel to be accurately and robustly labeled as one of a known set of classes. Hyperspectral image classification has been applied to both urban and agricultural scenery. Various methods have been developed for this application.
A structured dictionary-based model is proposed for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples.
- A. Soltani-Farani, H. R. Rabiee, and S. A. Hosseini, “Spatial-Aware Dictionary Learning for Hyperspectral Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 527-541, Jan. 2015 (pdf)
- This code is written for MATLAB and contains routines for several hyperspectral image classification methods used in the above publication. Please cite the above work if you use this software and contact first author in case of any problems.
- Ali Soltani-Farani, Abbas Hosseini, Hamid R. Rabiee