Feature extraction and classification for hyperspectral remote sensing images

Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing data at very high both spectral and spatial resolutions. Many techniques are developed to explore the spectral information and the spatial information of these data. In particular, feature extraction (FE) aimed at reducing the dimensionality of hyperspectral data while keeping as much spectral information as possible is one of methods to preserve the spectral information, while morphological profile analysis is the most popular methods used to explore the spatial information. Hyperspectral sensors collect information as a set of images represented by hundreds of spectral bands. While offering much richer spectral information than regular RGB and multispectral images, the high dimensional hyperspectal data creates also a challenge for traditional spectral data processing techniques. Conventional classification methods perform poorly on hyperspectral data due to the curse of dimensionality (i.e. the Hughes phenomenon: for a limited number of training samples, the classification accuracy decreases as the dimension increases). Classification techniques in pattern recognition typically assume that there are enough training samples available to obtain reasonably accurate class descriptions in quantitative form. However, the assumption that enough training samples are available to accurately estimate the class description is frequently not satisfied for hyperspectral remote sensing data classification, because the cost of collecting ground-truth of observed data can be considerably difficult and expensive. In contrast, techniques making accurate estimation by using only small training samples can save time and cost considerably. The small sample size problem therefore becomes a very important issue for hyperspectral image classification. Very high-resolution remotely sensed images from urban areas have recently become available. The classification of such images is challenging because urban areas often comprise a large number of different surface materials, and consequently the heterogeneity of urban images is relatively high. Moreover, different information classes can be made up of spectrally similar surface materials. Therefore, it is important to combine spectral and spatial information to improve the classification accuracy. In particular, morphological profile analysis is one of the most popular methods to explore the spatial information of the high resolution remote sensing data. When using morphological profiles (MPs) to explore the spatial information for the classification of hyperspectral data, one should consider three important issues. Firstly, classical morphological openings and closings degrade the object boundaries and deform the object shapes, while the morphological profile by reconstruction leads to some unexpected and undesirable results (e.g. over-reconstruction). Secondly, the generated MPs produce high-dimensional data, which may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. Last but not least, linear features, which are used to construct MPs, lose too much spectral information when extracted from the original hyperspectral data. In order to overcome these problems and improve the classification results, we develop effective feature extraction algorithms and combine morphological features for the classification of hyperspectral remote sensing data. The contributions of this thesis are as follows. As the first contribution of this thesis, a novel semi-supervised local discriminant analysis (SELD) method is proposed for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (Local Linear Feature Extraction Methods (LLFE)) and supervised method (Linear Discriminant Analysis (LDA)) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Our second contribution is the application of morphological profiles with partial reconstruction to explore the spatial information in hyperspectral remote sensing data from the urban areas. Classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction, which means that object size is often incorrectly represented. Morphological profiles with partial reconstruction improve upon both classical MPs and MPs with reconstruction. The shapes of objects are better preserved than classical MPs and the size information is preserved better than in reconstruction MPs. A novel semi-supervised feature extraction framework for dimension reduction of generated morphological profiles is the third contribution of this thesis. The morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. To the best of our knowledge the use of semi-supervised feature extraction methods for the generated morphological profiles has not been investigated yet. The proposed generalized semi-supervised local discriminant analysis (GSELD) is an extension of SELD with a data-driven parameter. In our fourth contribution, we propose a fast iterative kernel principal component analysis (FIKPCA) to extract features from hyperspectral images. In many applications, linear FE methods, which depend on linear projection, can result in loss of nonlinear properties of the original data after reduction of dimensionality. Traditional nonlinear methods will cause some problems on storage resources and computational load. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which estimates the eigenvectors through iteration. Without performing eigen decomposition on the Gram matrix, our approach can reduce the space complexity and time complexity greatly. Our last contribution constructs MPs with partial reconstruction on nonlinear features. Traditional linear features, on which the morphological profiles usually are built, lose too much spectral information. Nonlinear features are more suitable to describe higher order complex and nonlinear distributions. In particular, kernel principal components are among the nonlinear features we used to built MPs with partial reconstruction, which led to significant improvement in terms of classification accuracies. The experimental analysis performed with the novel techniques developed in this thesis demonstrates an improvement in terms of accuracies in different fields of application when compared to other state of the art methods

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

The Cyrilka Cave-the longest crevice-type cave in Czechia: structural controls, genesis, and age

The Cyrilka Cave is the second longest pseudokarst cave and the longest crevice-type cave in Czechia. Developed within the headscarp area of a deep-seated landslide, the cave became a focus of scientific research in recent years when new passages were discovered. Structural analysis provided a general tectonic plan of the cave, as well as more detailed data on geometry and kinematics of the relaxed rock massif. The primary structure of NNE- to ENE-striking bedding is broken by a system of NNE-striking fissures interconnected by two continuous ENE-striking dextral fracture zones. Abundant signs of recent sinistral strike-slips within the rock massif represent a bold structural feature of the cave. Along with DEM imaging and a detailed survey of the cave, 2-D and 3-D ERT measurements completed an image of the main predispositions and revealed the internal structure of the slope deformation. These measures also detected unknown crevices above the existing headscarp, which indicate the retrograde evolution of the landslide. Methodologically, we used the 3-D electrical resistivity tomography in the incoherent sedimentary flysch rocks for the first time. Based on radiocarbon dating of the stalactite core, the minimum age of the cave is up to 19,900 +/- 280 cal BP, which is the oldest age detected in the area of the Outer Flysch Carpathians so far; we thoroughly discuss further indirect evidence indicating a probable Late Pleistocene age of the cave.Web of Science47339237

Morphological and immunochemical characterization of the pollen grains of Chenopodium album L. (Chenopodiaceae) in a temperate urban area in Argentina

Chenopodium album es una hierba cosmopolita, anual, muy polimórfica, que crece en forma espontánea en baldíos con suelos modificados de los barrios periféricos de Bahía Blanca. En esta ciudad, el periodo de floración es principalmente entre febrero y marzo, que coincide con la mayor concentración de este tipo de polen en la atmósfera. El objetivo de este estudio fue caracterizar morfológica e inmunoquímicamente a los granos de polen de Chenopodium album obtenidos en diferentes zonas del área urbana de la Bahía Blanca. Las muestras fueron colectadas en tres zonas de la ciudad. La estructura y la morfología de los granos fueron analizados con microscopía óptica y electrónica. Los perfiles proteicos y antigénicos fueron estudiados mediante Tricine-SDS-PAGE y western blot, empleando un suero policlonal obtenido en conejo. El análisis morfológico mostró diferencias significativas en relación al diámetro de los granos de polen en una de las áreas estudiadas. En esa misma área se encontraron diferencias en la expresión de proteínas, aunque el perfil antigénico fue conservado. Estas variaciones en la morfología y en el perfil proteico podrían ser causadas por los efectos de las condiciones ambientales sobre el polen y la presencia de contaminantes urbanos provenientes del tráfico vehicular.Chenopodium album is a very polymorphic, cosmopolitan, annual herb that grows spontaneously in modified soils in wasteland in the outlying urban zones of Bahía Blanca. In this city, the flowering period is mainly during February and March, which coincides with the highest concentrations of this pollen type in the atmosphere of the city. The objective of this study was to characterize the pollen grains of Chenopodium album, both morphologically and immunochemically, that were obtained from three different zones in the urban area of Bahía Blanca. Samples were collected from the three separate zones in the city that were far apart. The structure and morphology of the grains were analyzed using light and electron microscopy. The protein and antigenic profiles were studied with Tricine-SDS-PAGE and western blot with a polyclonal rabbit serum, respectively. The morphological analysis showed significant differences in relation to the diameters of the pollen grains in one of the studied areas. Differences in the protein expression were seen for the same area although the antigenic profile was conserved. The variations in the morphology and the protein profile may be caused by the effect of environmental conditions on the pollen, and the presence of urban contaminants from vehicular traffic.Fil: Bianchimano, Andrea Susana. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Laboratorio de Inmunología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Murray, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias Biológicas y Biomédicas del Sur. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Instituto de Ciencias Biológicas y Biomédicas del Sur; ArgentinaFil: Aztiria, María Eugenia. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Laboratorio de Inmunología; ArgentinaFil: Montes, Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias Biológicas y Biomédicas del Sur. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Instituto de Ciencias Biológicas y Biomédicas del Sur; ArgentinaFil: Calfuán, Melina Lorena. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Prat, María Inés. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Laboratorio de Inmunología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201