Reflective Hyperspectral Imagery Research Articles

Metrics of spectral image complexity with application to large area search

Spectral image complexity is an ill-defined term that has been addressed previously in terms of dimensionality, multivariate normality, and other approaches. Here, we apply the concept of the linear mixture model to the question of spectral image complexity at spatially local scales. Essentially, the "complexity" of an image region is related to the volume of a convex set enclosing the data in the spectral space. The volume is estimated as a function of increasing dimensionality (through the use of a set of endmembers describing the data cloud) using the Gram Matrix approach. It is hypothesized that more complex regions of the image are composed of multiple, diverse materials and will thus occupy a larger volume in the hyperspace. The ultimate application here is large area image search without a priori information regarding the target signature. Instead, image cues will be provided based on local, relative estimates of the image complexity. The technique used to estimate the spectral image complexity is described and results are shown for representative image chips and a large area flightline of reflective hyperspectral imagery. The extension to the problem of large area search will then be described and results are shown for a 4-band multispectral image.

Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging

Automated discrimination of fruits with canker from other fruit with normal surface and different type of peel defects has become a helpful task to enhance the competitiveness and profitability of the citrus industry. Over the last several years, hyperspectral imaging technology has received increasing attention in the agricultural products inspection field. This paper studied the feasibility of classification of citrus canker from other peel conditions including normal surface and nine peel defects by hyperspectal imaging. A combination algorithm based on principal component analysis and the two-band ratio (Q(687/630)) method was proposed. Since fewer wavelengths were desired in order to develop a rapid multispectral imaging system, the canker classification performance of the two-band ratio (Q(687/630)) method alone was also evaluated. The proposed combination approach and two-band ratio method alone resulted in overall classification accuracy for training set samples and test set samples of 99.5%, 84.5% and 98.2%, 82.9%, respectively. The proposed combination approach was more efficient for classifying canker against various conditions under reflectance hyperspectral imagery. However, the two-band ratio (Q(687/630)) method alone also demonstrated effectiveness in discriminating citrus canker from normal fruit and other peel diseases except for copper burn and anthracnose.