Two HyMap images acquired over the same lignite open-pit mining site in Sokolov, Czech Republic, during the
summers of 2009 and 2010 (12 months apart), were investigated in this study. The site selected for this research is one of
three test sites (the others being in South Africa and Kyrgyzstan) within the framework of the EO-MINERS FP7 Project
(http://www.eo-miners.eu). The goal of EO-MINERS is to "integrate new and existing Earth Observation tools to
improve best practice in mining activities and to reduce the mining related environmental and societal footprint".
Accordingly, the main objective of the current study was to develop hyperspectral-based means for the detection of small
spectral changes and to relate these changes to possible degradation or reclamation indicators of the area under
investigation. To ensure significant detection of small spectral changes, the temporal domain was investigated along with
careful generation of reflectance information. Thus, intensive spectroradiometric ground measurements were carried out
to ensure calibration and validation aspects during both overflights. The performance of these corrections was assessed
using the Quality Indicators setup developed under a different FP7 project-EUFAR (http://www.eufar.net), which
helped select the highest quality data for further work. This approach allows direct distinction of the real information
from noise. The reflectance images were used as input for the application of spectral-based change-detection algorithms
and indices to account for small and reliable changes. The related algorithms were then developed and applied on a
pixel-by-pixel basis to map spectral changes over the space of a year. Using field spectroscopy and ground truth
measurements on both overpass dates, it was possible to explain the results and allocate spatial kinetic processes of the
environmental changes during the time elapsed between the flights. It was found, for instance, that significant spectral
changes are capable of revealing mineral processes, vegetation status and soil formation long before these are apparent to
the naked eye. Further study is being conducted under the above initiative to extend this approach to other mining areas
worldwide and to improve the robustness of the developed algorithm.
We present a method to evaluate point target detection algorithms. For any particular algorithm, a datacube without a target is evaluated with each pixel being assigned a score; the highest scores belong to those points which are potential false alarms. We then systematically implant a selected target signature into every pixel in the image and evaluate the resulting scores; the lowest scores are those pixels in which the target may be missed. ROC curve analysis can then be made. In this paper, we evaluate a new algorithm which we have developed; we use the evaluation of this algorithm as a paradigm for the efficacy of our algorithm evaluation tool.