An Adaptive Approach for the Progressive Integration of Spatial and Spectral Features When Training Ground-Based Hyperspectral Imaging Classifiers

The use of hyperspectrometers as analytical tools for determining surface material properties in ground-based applications introduces the need of integrating spatial and spectral hyperspectral cube components. A neural-network-based approach is presented in this paper with the aim of automatically adapting to the spatiospectral characteristics of samples in a problem domain so that the most efficient classification can be obtained. Its main application would be in inspection and quality control tasks. The system core is an Artificial Neural Network-based hyperspectral processing unit able to perform the online classification of the material based on the spatiospectral patterns provided by a set of pixels. A training adviser is implemented to automate the determination of the minimum spatial window size, as well as the optimum spectrospatial feature set leading to the desired classification in terms of the available ground truth. Several tests have been carried out on synthetic and real data sets. In particular, the proposed approach is used to discriminate samples of synthetic and real materials, where the pixel resolution implies that a material is characterized by spectral patterns of combinations of pixels.