Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles

Pedram Ghamisi, Jon Atli Benediktsson, Gabriele Cavallaro, Antonio Plaza
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Hyperspectral imaging systems have gained a great attention from researchers in the past few years. These systems use sensors, which acquire data mostly from the visible through the middle infrared wavelength ranges and can simultaneously capture hundreds of (narrow) spectral channels from the same area on the surface of the Earth. Thanks to the detailed spectral information provided by hyperspectral sensors, the possibility of accurately discriminating materials of interest with an increased
more » ... assification accuracy is increased. Furthermore, with respect to advances in hyperspectral imaging systems, the spatial resolution of recently operated sensors is getting finer, which enables analysis of small spatial structures in images. Without any doubt, classification (or mapping) can be considered as the backbone of most image interpretation in remote sensing. In general, supervised classification approaches classify input data by considering the spectral information (e.g., intensity value of each pixel for grayscale images or intensity vector for RGB or high-dimensional images) of the data to produce a classification map in order to discriminate different classes of interest, by using a set of representative samples for each class, referred to as training samples. This way, by using a combination of training followed by classification, maps are produced from imagery. However, most of the existing classification techniques have been developed for the analysis of multispectral images, and consequently, they are not usually efficient for the classification of hyperspectral images, which can provide a detailed spectral information. This brings up the question whether the currently available classification techniques will be able to handle high-dimensional data. The main objective of this thesis is the development of efficient spectralspatial classification approaches in terms of classification accuracies. Beside the importance of classification accuracies, another critical issue for the purpose of hyperspectral image classification is simplicity and speed of the applied approaches. Therefore, in this thesis, a special emphasis is given on proposing robust techniques in terms of classification accuracies as well as being fast. In
doi:10.1109/jstars.2014.2298876 fatcat:nxj4xswdlvc3bb47wujib2lahi