CUED Publications database

Progressive classification in the compressed domain for large EOS satellite databases

Castelli, V and Li, CS and Turek, J and Kontoyiannis, I (1996) Progressive classification in the compressed domain for large EOS satellite databases. In: UNSPECIFIED pp. 2199-2202..

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We introduce a new framework for classifying large images that is more accurate and less computationally expensive than the classical pixel-by-pixel approach. This approach, called progressive classification, is well suited for analyzing large images, such as multispectral satellite scenes, compressed with wavelet-based or block-transform-based transformations. These transformations produce a multiresolution pyramid representation of the data. A progressive classifier analyzes the image at the coarsest resolution level, and it decides whether each coefficient corresponds to a homogeneous block of pixels in the original image or to a heterogeneous block. In the first case it labels the block, in the second case it recursively analyzes the region of the image at the immediately finer resolution level. Computational efficiency, compared to the classical approach, results from examining a much smaller number of coefficients than the number of pixels in the original image. Thus, progressive classification is a prime candidate as a content-based search operator for remotely-sensed data.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 08 Jan 2018 20:12
Last Modified: 18 Aug 2020 12:42