Satellite Image Processing for Remote Sensing Applications

Abstract

This thesis investigates areas of image compression with particular reference to remote sensing imagery. The research described was carried out in four specific areas, namely, discrete cosine transform (DCT) for remote sensing imagery, lossless image compression based on conditional statistics, exploiting interband redundancy for remote sensing imagery, neural networks for lossless image compression.
The effect of using standard compression algorithm (JPEG's DCT) on the remote sensing image data is investigated. This involves visual and statistical assessment of the errors produced, both in the data itself, and with reference to the results of the processing (i. e., classification) normally performed using such data. It has been reported that the DCT characteristics can be modified to achieve a trade-off between compression ratio and pixel value error. It is feasible therefore that the user of remote sensing data could find a suitable compromise that could offer some of the compression benefits offered by the DCT, while. retaining sufficient accuracy of image data for the required applications.
An approach for lossless image compression using conditional statistics is investigated. That is encoding each pixel value with one of several variable-length codes depending on previous pixel values (context). The author's method achieved its aim by approximating the probability distribution function (PDF) for each context and coding the image data using arithmetic coding. Experimental results are included to show that this method has achieved some improvement in lossless image compression and can achieve an average bits per pixel lower than the zero-order entropy of the prediction-error image.
In the area of exploiting interband correlation for remote sensing imagery, two new techniques, namely joint entropy coding and interband prediction, are described. Joint entropy coding is based on the idea that to code a pair of pixel values from two different bands is more effective than to code them individually if there is interband correlation among them. Interband prediction is based on the fact that the structure of one band data can generally give some information about the structure of other bands. The results demonstrate and compare the usefulness of both techniques in improving the overall lossless compression ratio for remote sensing imagery.
The idea of using neural networks for lossless image coding is introduced. A novel approach to pixel prediction based on a three-layer perceptron neural network using a backpropagation learning algorithm is described, which is aimed at improving the pixel prediction accuracy, thus improving the lossless compression ratio. Experimental results show this neural network approach consistently achieves better prediction than conventional linear prediction techniques in terms of minimizing the mean square error, although the results for the overall compression ratio are not significantly improved.