The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 11–15, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-11-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 11–15, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-11-2017

  26 Sep 2017

26 Sep 2017

THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

A. H. Ahrari1, M. Kiavarz2, M. Hasanlou3, and M. Marofi4 A. H. Ahrari et al.
  • 1University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Tehran, Iran
  • 2University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Tehran, Iran
  • 3University of Tehran, Faculty of Survey, Department of Remote Sensing, Tehran, Iran
  • 4University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Tehran, Iran

Keywords: Multimodal Remote Sensing, Thermal and Visible Image Fusion, Wavelets Algorithms

Abstract. Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.