Dr. Beverly J. Thompson Profile

Dr. Beverly J. Thompson

Image Scientist at SAIC

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Proceedings Article | 29 June 2000 Paper

Multiscale retinex for improved performance in multispectral image classification

Beverly Thompson, Zia-ur Rahman, Stephen Park

Proceedings Volume 4041, (2000) https://doi.org/10.1117/12.390485

KEYWORDS: Image processing, Image classification, Transmittance, Image transmission, Image enhancement, Reflectivity, Atmospheric modeling, Image quality, RGB color model, Multispectral imaging

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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 different 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 also optimal for certain types of multispectral image classification.

Proceedings Article | 21 July 1999 Paper

Retinex preprocessing for improved multispectral image classification

Zia-ur Rahman, Stephen Park, Beverly Thompson

Proceedings Volume 3716, (1999) https://doi.org/10.1117/12.354715

KEYWORDS: Image classification, Image processing, Reflectivity, Image enhancement, Vegetation, Light sources and illumination, Nitrogen, Scene classification, Infrared signatures, Image acquisition

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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 discus the overall impact on multi-spectral image classification when the retinex image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has ben 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 ad thus provides better within-class variations with an 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 for the original images, without preprocessing, are much less similar.

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