Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis

Abstract—The same high dimensionality of hyperspectral imagery that facilitates detection of subtle differences in spectral response due to differing chemical composition also hinders the deployment of traditional statistical pattern-classification procedures, particularly when relatively few training samples are available. Traditional approaches to addressing this issue, which typically employ dimensionality reduction based on either projection or feature selection, are at best suboptimal for hyperspectral classification tasks. A divide-and-conquer algorithm is proposed to exploit the high correlation between successive spectral bands and the resulting block-diagonal correlation structure to partition the hyperspectral space into approximately independent subspaces. Subsequently, dimensionality reduction based on a graph-theoretic localitypreserving discriminant analysis is combined with classification driven by Gaussian mixture models independently in each subspace. The locality-preserving discriminant analysis preserves the potentially multimodal statistical structure of the data, which the Gaussian mixture model classifier learns in the reduced-dimensional subspace. Experimental results demonstrate that the proposed system significantly outperforms traditional classification approaches, even when few training samples are employed. Index Terms—Hyperspectral data, information fusion I

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Commercial forest species discrimination and mapping using image texture computed from WorldView-2 pan sharpened imagery in KwaZulu-Natal, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Forest species discrimination is vital for precise and dependable information, essential for commercial forest management and monitoring. Recently, the adoption of remote sensing approaches has become an important source of information in commercial forest management. However, previous studies have utilized spectral data or vegetation indices to detect and map commercial forest species, with less focus on the spatial elements. Therefore, this study using image texture aims to discriminate commercial forest plantations (i.e. A. mearnsii, E. dunnii, E. grandis and P. patula) computed from a 0.5m WorldView-2 pan-sharpened image in KwaZuluNatal, South Africa. The first objective of the study was to discriminate commercial forest species using image texture computed from a 0.5m WorldView-2 pan-sharpened image and the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. The results indicated that the image texture model (overall accuracy (OA) = 77%, kappa = 0.69) outperformed both the vegetation indices model (OA = 69%, kappa = 0.59) and raw spectral bands model (OA = 64%, kappa = 0.52). The most successful texture parameters selected by PLS-DA were mean, correlation, and homogeneity, which were primarily computed from the red-edge, NIR1 and NIR2 bands. Lastly, the 7x7 moving window was commonly selected by the PLS-DA model when compared to the 3x3 and 5x5 moving windows. The second objective of the study was to explore the utility of texture combinations computed from a fused 0.5m WorldView-2 image in discriminating commercial forest species in conjunction with the PLS-DA and Sparse Partial Least Squares Discriminate Analysis (SPLS-DA) algorithm. The accuracies achieved using SPLS-DA model, which performed variable selection and dimension reduction simultaneously yielded an overall accuracy of 86%. In contrast, the PLS-DA and variable importance in the projection (VIP) produced an overall classification accuracy of 81%. Generally, the finding of this study demonstrated the ability of image texture to precisely provide adequate information that is essential for tree species mapping and monitoring