Mr. J. Wesley Roberts Profile

J. Wesley Roberts

at Council for Scientific and Industrial Research

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SPIE Journal Paper | 1 May 2008

Assessment of image fusion procedures using entropy, image quality, and multispectral classification

J. Wesley Roberts, Jan van Aardt, Fethi Ahmed

JARS, Vol. 2, Issue 01, 023522, (May 2008) https://doi.org/10.1117/12.10.1117/1.2945910

KEYWORDS: Image fusion, Image quality, Synthetic aperture radar, Data fusion, Wavelets, Discrete wavelet transforms, Remote sensing, Image classification, Distortion, Multispectral imaging

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The use of disparate data sources within a pixel level image fusion procedure has been well documented for pan-sharpening studies. The present paper explores various image fusion procedures for the fusion of multi-spectral ASTER data and a RadarSAT-1 SAR scene. The research sought to determine which fusion procedure merged the largest amount of SAR texture into the ASTER scenes, while also preserving the spectral content. An additional application based maximum likelihood classification assessment was also undertaken. Three SAR scenes were tested namely, one backscatter scene and two textural measures calculated using grey level co-occurrence matrices (GLCM). Each of these were fused to the ASTER data using the following established approaches; Brovey transformation, Intensity Hue and Saturation, Principal Component Substitution, Discrete wavelet transformation, and a modified discrete wavelet transformation using the IHS approach. Resulting data sets were assessed using qualitative and quantitative (entropy, universal image quality index, maximum likelihood classification) approaches. Results from the study indicated that while all post fusion data sets contained more information (entropy analysis), only the frequency-based fusion approaches managed to preserve the spectral quality of the original imagery. Furthermore results also indicated that the textural (mean, contrast) SAR scenes did not add any significant amount of information to the post-fusion imagery. Classification accuracy was not improved when comparing ASTER optical data and pseudo optical bands generated from the fusion analysis. Accuracies range from 68.4% for the ASTER data to well below 50% for the component substitution methods. Frequency based approaches also returned lower accuracies when compared to the unfused optical data. The present study essentially replicated (pan-sharpening) studies using the high resolution SAR scene as a pseudo panchromatic band.

Proceedings Article | 9 October 2007 Paper

Investigating structural-spectral interactions in managed even-aged eucalyptus plantations using lidar and multispectral high resolution data

Jan van Aardt, Wesley Roberts, Michael Gebreslasie, Solomon Tesfamichael

Proceedings Volume 6742, 67420D (2007) https://doi.org/10.1117/12.737770

KEYWORDS: LIDAR, Data modeling, Earth observing sensors, High resolution satellite images, Modeling, Vegetation, Remote sensing, Multispectral imaging, Sensors, Biological research

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The main goal of this research was to investigate the structural-spectral interactions that exist in managed, homogeneous, even-aged Eucalyptus plantations through plot-level volume and basal area modelling in Kwazulu-Natal, South Africa. Eucalyptus plantations used in this study range between four and ten years old. Small-footprint light detection and ranging (lidar; ALTM 3033 two-return laser system; 0.2 mrad footprint, 33 kHz pulse rate) and IKONOS multispectral data were collected during the spring season of 2006. Structural characterisation of 15 m radius inventory plots were performed by derivation of independent model variables from plot-level distributions of a canopy height model, lidar point heights, multispectral data, and all data sets combined. The multispectral data and lidar data were used to characterise the structural differences across a gradient of plot volume and basal area values towards determination of structural variability contribution to spectral responses. These aspects relate to the implementation of accepted remote sensing data sources for forest structure assessment and how forest structure affects model outcomes. Results for plotlevel volume and basal area were encouraging using structural (lidar) data, with adjusted R² values of 0.94 and 0.82 for volume and basal area, respectively. Values for multispectral data were distinctly lower at 0.60 and 0.55 for the same dependent variables. Adjusted R² values for all data sets combined were only marginally better than lidar data with values of 0.95 and 0.88 for volume and basal area, respectively. Results show that lidar data are more amenable than a multispectral approach to forest structure assessment, although integration of the two data sources should be further investigated for scaling to larger areas.

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