Paper
29 December 2008 Forward-and-backward diffusion for hyperspectral remote sensing image smoothing and enhancement
Yi Wang, Ruiqin Niu, Huanfeng Shen, Xin Yu
Author Affiliations +
Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72851C (2008) https://doi.org/10.1117/12.815928
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
Among all enhancement techniques being developed over the past two decades, anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. The elegant property of the technique is that it can enhance images by reducing undesirable intensity variability within the objects in image, while improving signal-to-noise ratio (SNR) and enhancing the contrast of the edges in scalar and, more recently, in vector-valued images, such as color, multispectral and hyperspectral imagery. In this paper, we firstly analyze two complementary schemes-variational methods and nonlinear diffusion partial differential equations (PDEs), in terms of edge enhancement. Based on these analyses, a general flexible class of hyperspectral forward-and-backward (FAB) diffusion process will be proposed, which can achieve the main requirements for edgepreserving regularization with image enhancement. In addition, we use additive operator splitting (AOS) scheme to speedup the numerical evolution of the nonlinear diffusion equation with respect to traditional explicit schemes. The performance of the vector-valued FAB diffusion PDE is studied using some hyperspectral remote sensing images. Experimental results on these images are shown the validity and effectiveness of the proposed method.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi Wang, Ruiqin Niu, Huanfeng Shen, and Xin Yu "Forward-and-backward diffusion for hyperspectral remote sensing image smoothing and enhancement", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72851C (29 December 2008); https://doi.org/10.1117/12.815928
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Diffusion

Image enhancement

Image processing

Hyperspectral imaging

Remote sensing

Anisotropic diffusion

Image segmentation

Back to Top