Study of on-board compression of earth resources data

The current literature on image bandwidth compression was surveyed and those methods relevant to compression of multispectral imagery were selected. Typical satellite multispectral data was then analyzed statistically and the results used to select a smaller set of candidate bandwidth compression techniques particularly relevant to earth resources data. These were compared using both theoretical analysis and simulation, under various criteria of optimality such as mean square error (MSE), signal-to-noise ratio, classification accuracy, and computational complexity. By concatenating some of the most promising techniques, three multispectral data compression systems were synthesized which appear well suited to current and future NASA earth resources applications. The performance of these three recommended systems was then examined in detail by all of the above criteria. Finally, merits and deficiencies were summarized and a number of recommendations for future NASA activities in data compression proposed

An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding

A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed

Hybrid Techniques On Color And Multispectral Image For Compression

Image Compression is a technique to reduce the number of bits required to represent and store an image. This technique is also used to compress two dimensional color shapes without loss of data as well as quality of the Image. Even though Simple Principal Component Analysis can apply to make enough compression on multispectral image, it needs to extend another version called Enhanced PCA(E-PCA). The given multispectral image is converted into component image and transformed as Column Vector with help of E-PCA. Covariance matrix and eigen values are derived from vector. Multispectral images are reconstructed using only few principal component images with the largest variance of eigen value. Then the component image is divided into block. After finding block sum value, mean value, the number of bits required to represent an image can be reduced by E-BTC model. The features are extracted and constructed in Table form. The proposed algorithm is repeated for all multispectral images as well as color image in the database. Finally, compression ratio table is generated. This proposed algorithm is tested and implemented on various parameters such as MSE, PSNR. These experiments are initially carried out on the standard color image and are to be followed by multispectral imager using MATLAB

Fast Lossless Compression of Multispectral-Image Data

An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive-filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general

Band Ordering in Lossless Compression of Multispectral Images

In this paper, we consider a model of lossless image compression in which each band of a multispectral image is coded using a prediction function involving values from a previously coded band of the compression, and examine how the ordering of the bands affects the achievable compression. We present an efficient algorithm for computing the optimal band ordering for a multispectral image. This algorithm has time complexity O(n2) for an n-band image, while the naive algorithm takes time &#x03A9(n!). A slight variant of the optimal ordering problem that is motivated by some practical concerns is shown to be NP-hard, and hence, computationally infeasible, in all cases except for the most trivial possibility. In addition, we report on our experimental findings using the algorithms designed in this paper applied to real multispectral satellite data. The results show that the techniques described here hold great promise for application to real-world compression needs

Subspace-Clustering-Based Multispectral Image Compression

This paper describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard PCA, this approach finds clusters in different subspaces of different dimension. Consequently, instead of representing all spectra in a single low-dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. For a given compression ratio, this tradeoff reduces the maximum reconstruction error dramatically. In the case of compressing multispectral images, this initial compression step is followed by lossless JPEG2000 compression in order to remove the spatial redundancy in the data as well

Quality criteria benchmark for hyperspectral imagery

Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data.We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data