Retinex Image Processing--Publications--SPIE 3716, Orlando, Florida,
April 1999.
Conference
Retinex Preprocessing for Improved Multi-spectral Image
Classification
Beverly Thompson, Zia-ur Rahman, Stephen Park
Abstract
The goal of multi-image classification is to identify and label
``similar regions'' within a scene. The ability to correctly classify a
remotely sensed multi-image of a scene is affected by the ability of the
classification process to adequately compensate for the effects of
atmospheric variations and sensor anomalies. Better classification may
be obtained if the multi-image is preprocessed before classification, so
as to reduce the adverse effects of image formation. In this paper, we
discuss the overall impact on multi-spectral image classification when
the retinex image enhancement algorithm is used to preprocess
multi-spectral images. The retinex is a multi-purpose image enhancement
algorithm that performs dynamic range compression, reduces the
dependence on lighting conditions, and generally enhances apparent
spatial resolution. The retinex has been successfully applied to the
enhancement of many different types of grayscale and color images. We
show in this paper that retinex preprocessing improves the spatial
structure of multi-spectral images and thus provides better within-class
variations than would otherwise be obtained without the preprocessing.
For a series of multi-spectral images obtained with diffuse and direct
lighting, we show that without retinex preprocessing the class spectral
signatures vary substantially with the lighting conditions. Whereas
multi-dimensional clustering without preprocessing produced one-class
homogeneous regions, the classification on the preprocessed images
produced multi-class non-homogeneous regions. This lack of homogeneity
is explained by the interaction between different agronomic treatments
applied to the regions: the preprocessed images are closer to ground
truth. The principle advantage that the retinex offers is that for
different lighting conditions classifications derived from the retinex
preprocessed images look remarkably ``similar'', and thus more
consistent, whereas classifications derived from the original images,
without preprocessing, are much less similar.
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