Data dependent adaptation for improved classification of hyperspectral imagery

The per-pixel spectral information present in hyperspectral imagery (HSI) is typically of very high dimensionality due to the presence of hundreds of continuous narrow spectral bands. Although such high dimensional data has the potential of providing useful information for land-cover classification and mapping tasks, it is often also likely to result in ill-conditioned statistical formulations and reduced performance due to over-dimensionality problems. Much of the research in HSI analysis attempts to find appropriate dimensionality reduction and classification techniques that best exploit this high dimensional imagery. Conventional approaches to dimensionality reduction and classification look at the HSI holistically and attempt to find projections and decision rules that optimize a global criterion, such as the overall accuracy, Fisher´s ratio over all classes etc. In this paper, we propose an adaptation strategy that adapts conventional classifiers to re-focus on hard to recognize classes. After an appropriate “holistic” feature selection, the proposed adaptation helps identify additional features that best separate the most “confused” class pairs in the dataset. We demonstrate this data-dependent adaptation of conventional feature selection and classification methods results in improved classification performance.