PROCEEDINGS ARTICLE | May 19, 2016
Proc. SPIE. 9874, Remotely Sensed Data Compression, Communications, and Processing XII
KEYWORDS: Image compression, Hyperspectral imaging, Image classification, Computer programming, Feature extraction, Data compression, Hyperspectral imaging, Image classification, Principal component analysis, Signal to noise ratio, 3D image processing, Reconstruction algorithms
Most compression methods for hyperspectral images have been optimized to minimize mean squared errors. However,
this kind of compression method may not retain all discriminant information, which is important if hyperspectral images
are to be used to distinguish among classes. In this paper, we propose a two-stage compression method for hyperspectral
images with encoding residual discriminant information. In the proposed method, we first apply a compression method
to hyperspectral images, producing compressed image data. From the compressed image data, we produce reconstructed
images. Then we generate residual images by subtracting the reconstructed images from the original images. We also
apply a feature extraction method to the original images, which produces a set of feature vectors. By applying these
feature vectors to the residual images, we generate discriminant feature images which provide the discriminant
information missed by the compression method. In the proposed method, these discriminant feature images are also
encoded. Experiments with AVIRIS data show that the proposed method provides better compression efficiency and
improved classification accuracy than other compression methods.
