Document Type
Article
Language
eng
Publication Date
5-2014
Publisher
Institute of Electrical and Electronic Engineers (IEEE)
Source Publication
IEEE Transactions on Image Processing
Source ISSN
1057-7149
Abstract
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.
Recommended Citation
Paskaleva, Biliana S.; Godoy, Sebastian E.; Jang, Woo-Yong; Bender, Steven C.; Krishna, Sanjay; and Hayat, Majeed M., "Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks" (2014). Electrical and Computer Engineering Faculty Research and Publications. 619.
https://epublications.marquette.edu/electric_fac/619
ADA Accessible Version
Comments
Accepted version. IEEE Transactions on Image Processing, Vol. 23, No. 5 (May 2014): 2315-2327. DOI. © 2014 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.
Majeed M. Hayat was affiliated with University of New Mexico, Albuquerque at the time of publication.