Evaluation of gradient operators for hyperspectral image processing

Abstract

Gradient is an important image processing tool allowing to carry out edge detection, segmentation, and texture analysis. It is expected to provide significantly more accurate and complete information when combined with the hyperspectral imaging technology. And such gain in accuracy justifies the cost and complexity of a hyperspectral acquisition. But how to measure accuracy? This article presents our first study in the accuracy assessment of hyperspectral gradient, where an assessment protocol based on a basic edge detection task is proposed. This protocol is then used to evaluate the full-band gradient approaches, where the results suggest to improve the protocol to include more complexity. * In the context of Sobel and Di Zenzo filters, structuring element is equivalent to the filtering window.

Hyperspectral Pigment Dataset

“…The dataset published in this work is aimed at filling the gap. An earlier version of the dataset has been used to assess the quality of various spectral processing algorithms [10,11,12]. In this article, two examples of its use in IQA protocols for distance functions will be demonstrated to inspire readers in designing their own protocols.…”