Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

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.

I. Introduction

Image segmentation and edge detection for multispectral (MS) and hyperspectral (HS) images can be an inherently difficult problem since gray-scale images associated with individual spectral bands may reveal different edges. Segmentation algorithms for gray-scale images utilize basic properties of intensity values such as discontinuity and similarity [1]. Popular gray-scale edge detectors include Canny [2], Sobel [3], and Prewitt [1], to name just a few. The transition from a gray-scale to a multicolor image complicates edge detection significantly: the standard definition of a gray-scale edge as a “ramp” or “ridge” between two regions [1], p. 573 is no longer appropriate because a multicolor image has multiple image planes (channels) corresponding to different spectral bands. Moreover, depending on the composition of the scene, two distinct spectral (color) regions may exhibit the same intensity for one or more bands and, in this case, the edge between the two regions is termed isoluminant. An isoluminant edge is therefore characterized by a jump in color rather than a jump in intensity. As a result, isoluminant edges cannot be detected easily by a standard gradient-based operator because they usually do not exhibit an intensity ramp that can be estimated by the magnitude of such an operator [4]. (Examples of isoluminant edges will be shown in Section III-B).