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Unsupervised hyperspectral classification based on similarity graphs

: González Santiago, Jonathan; Schenkel, Fabian; Gross, Wolfgang; Middelmann, Wolfgang


Bruzzone, L. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image and Signal Processing for Remote Sensing XXVI : 21-25 September 2020, Online Only, United Kingdom
Bellingham, WA: SPIE, 2020 (Proceedings of SPIE 11533)
ISBN: 978-1-5106-3879-2
ISBN: 978-1-5106-3880-8
Paper 115330S, 19 pp.
Conference "Image and Signal Processing for Remote Sensing" <26, 2020, Online>
Conference Paper
Fraunhofer IOSB ()
hyperspectral imaging; similarity graphs; Spectral Clustering

Hyperspectral cluster analysis represents a powerful instrument for land cover classification. It consists of grouping hyperspectral pixels based on a similarity measure that determines the affinity level between data points. Many of the existing clustering methods are not suitable for hyperspectral data due mainly to the so called curse of dimensionality. The previous fact motivates researchers to develop new clustering algorithms for dealing with high dimensional data. Among these are the techniques based on Spectral Graph Theory (SGT). They regard objects as vertices and their pair-wise similarity as weighted edge to transform the clustering problem into a graph partition task. Their properties make them well-suited for datasets with arbitrary shape and high dimensionality. The current approach strives the unsupervised classification of hyperspectral imagery employing Similarity Graphs (SG). To achieve this goal, a superpixel-based segmentation using the Simple Linear Iterative Clustering (SLIC) algorithm is executed. It takes the input data and groups pixels considering their image proximity and spectral similarity. Subsequently, the superpixels are converted into a Similarity Graph G = (V, E) with vertex set V = V1, V2, ..., Vn, where n represents the vertex number. For this conversion, the Adjacency Matrix (AM) is constructed with the similarities between vertices. Consequently, the Laplacian Matrix (LM) is determined to embed the data points into a low-dimensional space. This embedding occurs after finding the eigenvalues and eigenvectors of the LM. At this point, the clustering algorithm groups relevant LM eigenvectors to generate the land cover map. Finally, a comparison between the classified maps and the results of directly applying the Hierarchical Agglomerative Custering (HAC) algorithm on the corresponding superpixels is executed. This analysis considers the correspondence of the results with reality and the magnitude of the Cohen’s Kappa coefficient. The proposed method uses two benchmark datasets to create land cover classification maps. The results show that the method is capable of accurately partitioning data points with moderate overlapping level, where established algorithms such as the HAC still experiences difficulties.