Effects of Lossy Compression on Hyperspectral Classification

Abstract

Rapid advancements in sensor technology have produced remotely sensed data with hundreds of spectral bands. As a result, there is now an increasing need for efficient compression algorithms for hyperspectral images. Modern sensors are able to generate a very large amount of data from satellite systems and compression is required to transmit and archive this hyperspectral data in most cases. Although lossless compression is preferable in some applications, its compression efficiency is around three [1–3]. On the other hand, lossy compression can achieve much higher compression rates at the expense of some information loss. Due to its increasing importance, many researchers have studied the compression of hyperspectral data and numerous methods have been proposed, including transform-based methods (2D and 3D), vector quantization [3–5], and predictive techniques [6]. Several authors have used principal component analysis to remove redundancy [7–9] and some researchers have used standard compression algorithms such as JPEG and JPEG 2000 for the compression of hyperspectral imagery [9–14]. The discrete wavelet transform has been applied to the compression of hyperspectral images [15, 16] and several authors have applied the SPIHT algorithm to the compression of hyperspectral imagery [17–23].