PE&RS September 1996

VOLUME 62, NUMBER 9
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
JOURNAL OF THE AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING

Peer-Reviewed Articles

1025 Image-Based Atmospheric Corrections-Revisited and Improved
Pat S. Chavez, Jr.

Abstract
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To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The dark-object subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image- based procedure that expands on the DOS model by including a simple multiplicative correction for the effect of atmospheric transmittance. 

1037 Restoration of Corrupted Optical Fuyo-1 (JERS-1) Data Using Frequency Domain Techniques
C.R. de Souza Filho, S.A. Drury, A.M. Denniss, R.W.T. Carlton, and D.A. Rothery

Abstract
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The image data collected by Fuyo-1's sensors covering the visible and the short wave infrared (SWIR) are affected by severe noise problems. The important narrow SWIR channels show the worst defects. Fourier analysis is used to characterize these artifacts in the frequency domain. A scene-dependent Fourier operator that is able to eliminate the major noise components is described. This involves the construction of a binary mask derived from the difference between the Fourier spectra of two channels containing noise signals at similar frequencies and amplitude. This mask is used to modulate frequency domain images, so removing all noise components while preserving real image data with minimum loss and distortion in the spatial domain. Fuyo-1 brightness saturation problems can also be minimized by applying a Gaussian contrast stretch to the Fourier spectra prior to image inversion. 

1049 Registration Techniques for Multisensor Remotely Sensed Images
Leila M.G. Fonseca and B.S. Manjunath

Abstract
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With the increase in the number of images collected every day from different sensors, automated registration of multisensor/multispectral images has become a very important issue. Given the density of the data, it is unlikely that a single registration scheme will work satisfactorily for all different applications. A possible solution is to integrate multiple registration algorithms into a rule-based artificial intelligence system so that appropriate methods for any given set of multisensor data can be automatically selected. The first step in the development of such an expert system for remote sensing application would be to obtain a better understanding and characterization of the various existing techniques for image registration. This is the main objective of this paper as we present a comparative study of some recent image registration methods. 

1057 The Use of Multiresolution Analysis and Wavelets Transform for Merging SPOT Panchromatic and Multispectral Image Data
Bruno Garguet-Duport, Jacky Girel, Jean-Marc Chassery, and Guy Patou

Abstract
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Several techniques have been developed to merge SPOT 10-m resolution panchromatic data (SPOT P) with simultaneously acquired 20-m resolution multispectral data (SPOT XS). A method allowing the use of 10-m resolution XS data while conserving the spectral properties of the original 20-m data is presented. This method uses a multiresolution analysis procedure based upon the wavelets transform; it is applied to remotely sensed SPOT P and SPOT XS images of the river junction Arc-Isere (France). This new method is compared with the IHS method and P+XS method. The wavelets method is the one which least distorts the spectral characteristics of the data. The distortions are minimal and difficult to detect. 

1067 Multiresolution Wavelet Decomposition Image Merger of Landsat Thematic Mapper and SPOT Panchromatic Data
David A. Yocky

Abstract
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Spatially registered Landsat Thematic Mapper (TM) and SPOT panchromatic images were merged by combining multiresolution wavelet decomposition components from each, and then reconstructing a merged image using the inverse wavelet transform. Three wavelet merging techniques were compared to the intensity- hue-saturation merging technique. The comparison results show the wavelet merger providing greater flexibility and the potential for higher accuracy for combining and preserving spectral-spatial information for remotely sensed data and their applications. 

1075 Multispectral Imagery Band Sharpening Study
Jim Vrabel

Abstract
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The fusion of multisensor and multiresolution satellite imagery is an effective means of exploiting the complimentary nature of various image types. With band sharpening (a type of imagery fusion), higher spatial resolution panchromatic data is fused with lower resolution multispectral imagery (MSI). This function creates a product with the spectral characteristics of the MSI and a spatial resolution approaching that of the panchromatic image (effective ground- sample distance, or GSD). MSI of 10 m to 30 m was sharpened with 5- to 15-m imagery (sharpening factors of 2:1 to 6:1) using four algorithms. The research goals were to determine the validity of the concept of 'effective GSD'; determine the relative utility of band sharpening by different factors; and compare the relative effectiveness of different band sharpening algorithms. 

1085 An Empirical Investigation of Image Resampling Effects Upon the Spectral and Textural Super-vised Classification of a High Spatial Resolution Multispectral Image
O. Dikshit and D.P. Roy

Abstract
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Spectral and textural maximum-likelihood classifications are performed upon unresampled and upon bilinear and cubic convolution resampled versions of the image. The texture classifications use spectral training data and additional texture training data calculated using the grey-level difference histogram algorithm. The resampling algorithms increase the overall classification accuracies in a statistically significant manner which vary between individual classes. These results are explained by consideration of the interaction between the local smoothing properties of the resampling algorithms and the grey-level structure of the image. The results indicate that spectral and textural classification procedures may be applied to images, after they have been resampled, without a reduction in the classification accuracy, and that textural classification procedures should use class training statistics collected from the resampled image.