Title :
Object-based SAR image compression using vector quantization
Author :
Venkatraman, Mahesh ; Kwon, Heesung ; Nasrabadi, Nasser M.
Author_Institution :
Berkeley Concept Res. Corp., CA, USA
fDate :
10/1/2000 12:00:00 AM
Abstract :
A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.
Keywords :
image coding; neural nets; radar computing; radar imaging; radar target recognition; synthetic aperture radar; vector quantisation; background information; backpropagation; layer segmentation; lossy compression; multiple object planes; nonlinear neural network predictor; object-based SAR image compression; object-plane-based encoding method; software simulation; target classification; target recognition; variable-rate residual VQ algorithm; vector quantization; Bandwidth; Image coding; Laboratories; Object detection; Powders; Pulse modulation; Satellite ground stations; Synthetic aperture radar; Target recognition; Vector quantization;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on