Retinex Image Processing--Publications--SPIE 4041, Orlando, Florida,
April 2000.
Conference
A Multiscale Retinex for Improved Performance in Multispectral Image
Classification
Abstract
Image preprocessing is useful in helping to identify ``spectral response
patterns" for certain types of image classification problems. The
common artifacts in remotely sensed images are caused by the blurring
due to the optics of the image gathering device, illumination
variations, and the radiative transfer of the atmosphere. The
Multi-Scale Retinex (MSR) image enhancement algorithm that provides
dynamic range compression, reduced dependence on lighting conditions,
and improved (perceived) spatial resolution has proven to be an
effective tool in the correction of image degradations such as those in
remote sensing images. In this paper, we measure the improvement in
classification accuracy due to the application of the MSR algorithm. We
use simulated images generated with different scene irradiance and with
known ground truth data. The simulation results show that,
despite the degree of image degradation due to changes in atmospheric
irradiance, classification error can be substantially reduced by
preprocessing the image data with the MSR. Furthermore we show that,
similar to the results achieved in previous work, the classification
results obtained from the MSR preprocessed images for various scene
irradiance are more similar to each other than are the classification
results for the original unprocessed images. This is evident in the
observed visual quality of the MSR enhanced images even before
classification is performed, and in the difference images obtained by
comparing image data under different irradiance conditions. We conclude
that the application of the MSR algorithm results in improved visual
quality and increased spatial variation of multispectral images that is
Download
Compressed Postscript (2.5M) or
Postscript (14.7M)
or
PDF (1.2M)
Retinex Publications Page