N 94-28263 Wavelet Compression Techniques for Hyperspectral

Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data [1,2,3,4], we are not aware of any published results for hyperspectral data. In this paper we compare three algorithms for hyperspectral data compression. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder (using the algorithm described in [5]) was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm. These algorithms were tested on several data sets obtained from the TRW imaging spectrometer (TRWlS). This sensor provides measurements from 90 uniform width spectral bands which cover a wavelength range from approximately 400 nm to 800 nm, and is mounted in a helicopter or small plane. Spectra are obtained simultaneously from a linear array of 256 spatial resolution cells. Platform motion is utilized to scan this array, thus obtaining spatial samples in a second spatial dimension. A typical TRWIS data set consists of a 90x256x450 array of one byte samples. Although signal to noise ratio (SNR) and related mean square distortion metrics are convenient and widely used, their relevance to practical utility or perceptual quality is uncertain. This is of particular concern with respect to hyperspectral data, since the art of interpreting and utilizing this data is still developing, To supplement SNR measurements for the different algorithms, we also applied example pixel classification and image segmentation algorithms to the reconstructed data sets in order to assess the impact of compression losses on automatic data exploitation. These applications include pixel classification using a k-means algorithm and region based spectral image segmentation. Our results showed substantial differences in the performance of the three algorithms. The spectral decorrelation algorithm produces the best results, but also requires the most computational effort. The three dimensional wavelet algorithm's performance came in second, but well ahead of the band independent algorithm. These results clearly demonstrate the importance of exploiting the spectral redundancy. Spectral decorrelation performs best because the transform is optimally matched to the data, whereas the wavelet transform is suboptimal but computationally