Fusion and quality analysis for remote sensing images using contourlet transform
Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Defense and security; Image fusion; Medical imaging; Remote sensing; Spectral resolution; Wavelets
Controls and Control Theory | Electrical and Computer Engineering | Electrical and Electronics | Electronic Devices and Semiconductor Manufacturing | Nanotechnology Fabrication | Power and Energy | Signal Processing | Systems and Communications
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Fusion and quality analysis for remote sensing images using contourlet transform.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 8743