Compression algorithms for classification of remotely sensed images

The paper presents a comparison of the principal lossy compression algorithms, Vector Quantization (VQ), JPEG and Wavelets (WV) posterior KLT applied to multispectral remotely sensed images and evaluated by the classification algorithm KNN. The main goal of the compression of remotely sensed images is a reduction of the huge requirements for downlink and storage. The Karhunen Loeve Transform first removes the interband correlation to produce the principal components of the image which are then compressed by the principal algorithms. The quality evaluation was done by a supervised classification with the well known algorithm K-NN for remote sensing applications and the MSE for visual aspects. The obtained results of these accurate and particular analysis of the current compression techniques are quite surprisingly compared to other recent works. INTRODUCTION Multispectral remotely-sensed (R-S) images have nowadays huge storage requirements to archive high resolutions and occupy large bandwidth during downlinking. Moreover the wide use of these images for viewing, analyzing over many spectral bands, classification and storing require efficient methods to reduce the redundant information. Data compression plays therefore an important role in analysis and classification of remotely sensed imagery, reducing transmission time, bandwidth and storage requirements. R-S imaging applications include change detection where images of the same ground area are acquired and stored for long periods, earth monitoring and terrain classification, while automated or semi-automated image processing tools are used to identify and classify agriculture areas, urban areas, forests etc. This images which require usually about 150 Mbytes, e.g. Thematic Mapper (TM), are acquired by satellite or aircraft mounted sensors and are in general stored or transmitted to ground stations without using any compression tool, thus requiring a very large bandwidth. Some interesting preliminary results have been archived in the field of lossless compression techniques [6], also extending the JPEG standard to R-S images [9, 3] while good results regarded VQ techniques. For the particular application, automatic classification of compressed multispectral remotely sensed images, there are still no results available in the literature which makes this work of high interest to the reader. METHODS Karhunen Loeve Transform Usually in remotely sensed multispectral images there is a large amount of interband correlation due to the co-located sensors and the spectral overlap of the bands, also the case in Landsat TM images. The most effective technique to exploit this correlation is the application of the Karhunen Loeve Transform (KLT) which produces the principal components of the image. The KLT which is an orthogonal transformation, provides the minimum mean square error (MSE) during decorrelation by discarding the high index coefficients in the transformed space and maximizes the energy in the fewest number of coefficients. The highest energy is concentrated in the transformed bands corresponding to the largest eigenvalues. The JPEG Algorithm As the content of this paper deals only with single-component (grayscale) images, we consider just the DCT based mode of operation, essentially the compression of a stream of 8x8 pixel blocks. For detailed information on this algorithm consider [13]. Vector Quantization A k-dimensional memoryless vector quantizer (VQ) consists of an encoder α which assigns to each input vector x = (x0, x1, ...., xi-1) a channel symbol α(x) in a specified channel symbol set M, called codebook, and a decoder β assigning to each channel symbol z in M an output value x’ = (x’0, x’1, ...., x’i-1) in a reproduction alphabet A [5]. The generation of the codebook is done by the well known LBG algorithm [8]. In the particular application of image coding the VQ operates on small, 2-dimensional rectangular block samples of usually 3x3 or 4x4 image pixels. The decoded image quality is mainly dependent on the blockand codebook size. Typical characteristics of decoded images are in particular the poorly reproduced edges and the known “blocky” effect due to codeword edges where some particular solutions, e.g. the construction of segmented codes and separated codebooks for edge and texture information have been studied [4, 11]. Wavelet Transform A novel technique for image data compression is based on adaptive vector quantization of wavelet coefficients. This technique promise high compression rates at good image quality while it performs usually better than the JPEG and VQ, both in quantitative (MSE) and qualitative terms, absence of blockness distortion as known from VQ and JPEG. Several methods are presented in literature for wavelet-based image compression while a certain number of approaches propose vector quantization of the wavelet transformed coefficients in different subbands [14, 1]. The wavelet representation of an image is composed by the approximation of the signal at low resolution and a set of details at several resolutions. The image at low resolution is a low-pass version of the original one, while the details contain the information at high frequencies. The signal of each subband is found through an iterative algorithm which decompose the original signal into four more detailed ones where each signal contains the information regarding a particular frequency band and orientation, see figure 1. The reconstruction algorithm is strongly related to the decomposition technique while the complete signal is found again through a pyramidal algorithm, taking into account the lowpass signal and the set of details.