Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

Abstract

Abstract-This paper describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.

Morphological feature extraction for automatic registration of multispectral images

“…To address this issue, we simply order the bins in the histogram in descending order (i.e., the lowest measures of change are placed first and the higher measures of change are placed last in the histogram) prior to spectral similarity matching using the SID measure. This issue is further discussed in [6].…”

“…If the pair of images to be registered are rotated against each other, as is indeed the case with ALI-Hyperion pairs, comparing the SOMPs directly (by using, e.g., a measure of spectral similarity) does not make sense, as opposed to the registration of Landsat data, for which a trivial rotation close to 0 degrees is expected. In fact, the SOMPs can be seen as histograms that indicate the degree of variation at different scales and orientations (see [6] for a more detailed explanation). Thus, if we calculate such a histogram for a certain pixel, then rotate the image, and recalculate the histogram, the order of the histogram bins (which show a measure of change of the pixel with regards to surrounding features in the scene at different scales and orientations) would be different, even though they refer to the same image pixel.…”

Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

“…Mathematical morphology is a standard image processing technique that provides a remarkable framework to achieve the desired integration of spatial and spectral responses [8]. First, we describe the standard morphological algorithm.…”

“…Morphological analysis has been successfully used in previous research to analyze hyperspectral data sets [8]. The morphological algorithm selected in this work as a representative case study takes into account both the spatial and spectral information of the data in simultaneous fashion.…”

“…The main idea of the algorithm is to impose an ordering relation in terms of spectral purity in the set of pixel vectors lying within a spatial search window or structuring element (SE) around each image pixel vector [8]. To do so, we first define a cumulative distance between one particular pixel ( )…”