Advanced Techniques for Unsupervised Classification of Remote Sensing Hyperspectral Images
Aidin Hassanzadeh Advanced Techniques for Unsupervised Classi cation of Remote Sensing Hyperspectral Images Austin TX, U.S. 2019 117 p. Acta Universitatis Lappeenrantaensis 853 Diss. Lappeenranta Lahti University of Technology LUT ISBN 978 952 335 370 1 ISBN 978 952 335 371 8 (PDF) ISSN L 1456 4491 ISSN 1456 4491 Hyperspectral images consisting of a broad range of contiguous spectral bands ought to be a valuable tool for land cover type mapping. However, the coarse spatial resolutions of remote sensing Hyperspectral images makes a detailed semantic interpretation and pixel wise labeling complex. Indeed, the lack of labeled training data is a common problem that directly a ects the application of supervised classi cation of land cover type mapping. Due to the speci c conditions rooted in remote sensing hyperspectral imagery, remote sensing hyperspectral images are from high spectral dimensions covering large spatial extents. HSIs generally come with complex inter variable non linear dependencies. The lack of labeled data, the intrinsic high dimensionality and the spectral non linearity present in remote sensing hyperspectral images make any pursuant land cover type classi cation a challenging and uneasy task. The primary objective of the present dissertation is to design and to implement new techniques for the classi cation of remote sensing hyperspectral images that can e ectively perform land cover mapping. To achieve this objective, particular focus is placed on the integration of non linear manifold learning with unsupervised classi cation. In this regard, this dissertation incorporates four main contributions, each achieving satisfactory results in terms of accuracy and precision of classi cation. First, an outlier robust geodesic K mean algorithm for unsupervised classi cation of hyperspectral imaging data is proposed. The proposed algorithm expands the standard K means algorithm by an adaptive density based geodesic distance that is robust to the presence of outliers and the data with varying cluster shapes. Second, a framework of multi manifold spectral clustering based on a Weighted Principal Component Analysis is proposed. Unsupervised classi cation via multi manifold learning has been an active area in several pattern recognition applications, but it has not been e ectively employed in remote sensing hyperspectral image classi cation tasks. In this dissertation, multi manifold spectral clustering is explored as applied in hyperspectral image classi cation. Third, a new variant of multi manifold spectral clustering is proposed that exploits the Contractive Autoencoder for tangent estimation. The multi manifold spectral clustering by the Contractive Autoencoder makes a multi manifold based clustering model that is more robust to local data variations and the presence of noisy data. Fourth, a bipartite graph based sequential spectral clustering algorithm is proposed for the unsupervised classi cation of large scale remote sensing hyperspectral imaging data. The proposed sequential Spectral Clustering deals with the scalability limitations of the standard Spectral Clustering algorithm and extends its applicability to real world large sample size hyperspectral images. To validate the developed classi cation algorithms in this dissertation, several publicly available remote sensing hyperspectral images are leveraged, including hyperspectral images provided by the standard and widely used instruments such as NASA's Airborne Visible Infra Red Imaging Spectrometer (AVIRIS) and the Re ective Optics Spectrographic Imaging System (ROSIS) The experiments on real world hyperspectral images result in the conclusion that the proposed techniques can assist in land cover type mapping by remote sensing hyperspectral image classi cation.