New approaches on dimensionality reduction in hyperspectral images for classification purposes

This paper presents a quasi-unsupervised methodology to detect endmembers within an hyperspectral scene and to derive a pixel-wise classification on its basis. The endmember detection step takes as input an overcomplete spectral library, and detects the materials within a scene by analyzing derivative features under the sparsity assumption. The purest pixels for each detected material are then fed to a classifier based on synergetics theory, which is able to produce accurate classification maps on the basis of a restricted training dataset. As the classifier projects the image onto a subspace composed by the classes of interest found in the first step, a focused dimensionality reduction is performed in which every dimension is semantically meaningful

Sparse Methods for Hyperspectral Unmixing and Image Fusion

In recent years, the substantial increase in the number of spectral channels in optical remote sensing sensors allows more detailed spectroscopic analysis of objects on the Earth surface. Modern hyperspectral sensors are able to sample the sunlight reflected from a target on the ground with hundreds of adjacent narrow spectral channels. However, the increased spectral resolution comes at the price of a lower spatial resolution, e.g. the forthcoming German hyperspectral sensor Environmental Mapping and Analysis Program (EnMAP) which will have 244 spectral channels and a pixel size on ground as large as 30 m $\times$ 30 m. The main aim of this thesis is dealing with the problem of reduced spatial resolution in hyperspectral sensors. This is addressed first as an unmixing problem, i.e., extraction and quantification of the spectra of pure materials mixed in a single pixel, and second as a resolution enhancement problem based on fusion of multispectral and hyperspectral imagery. This thesis proposes novel methods for hyperspectral unmixing using sparse approximation techniques and external spectral dictionaries, which unlike traditional least squares-based methods, do not require pure material spectrum selection step and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. However, in previous works it has been shown that these methods suffer from some drawbacks, mainly from the intra dictionary coherence. To improve the performance of sparse spectral unmixing, the use of derivative transformation and a novel two step group unmixing algorithm are proposed. Additionally, the spatial homogeneity of abundance vectors by introducing a multi-look model for spectral unmixing is exploited.% Based on the above findings, a new method for fusion of hyperspectral images with higher spatial resolution multispectral images is proposed. The algorithm exploits the spectral information of the hyperspectral image and the spatial information from the multispectral image by means of sparse spectral unmixing to form a new high spatial and spectral resolution hyperspectral image. The introduced method is robust when applied to highly mixed scenarios as it relies on external spectral dictionaries. Both the proposed sparse spectral unmixing algorithms as well as the resolution enhancement approach are evaluated quantitatively and qualitatively. Algorithms developed in this thesis are significantly faster and yield better or similar results to state-of-the-art methods

Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion

Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not provide any spatial information about the sources and therefore additional information is needed to precisely locate the sources. In order to restore the spatial information of hyperspectral images we propose a hyperspectral and multispectral image fusion method based on spectral unmixing. The algorithm is tested with HyMAP image data consisting of 125 spectral bands and a simulated multispectral image consisting of 8 bands

Spectral Matching through Data Compression

This paper proposes to use compression-based similarity measures to cluster spectral signatures on the basis of their similarities. Such universal distances estimate the shared information between two objects by comparing their compression factors, which can be obtained by any standard compressor. Experiments on spectra, both collected in the field and selected from a hyperspectral scene, show that these methods may outperform traditional choices for spectral distances based on vector processing such as Spectral Angle, Spectral Information Divergence, Spectral Correlation, and Euclidean Distance

Sparse approximation, coherence and use of derivatives in hyperspectral unmixing.

Recently, it has been shown that the spectral unmixing can be regarded as a sparse approximation problem. In our studies we employ predefined dictionaries containing the measured spectra of different materials in a hyperspectral image, where for each pixel the abundance vector can be estimated solving the $\ell_1$ optimization problem. This results in an automation of the unmixing procedure and enables using complex overcomplete dictionaries. However, the reflectance spectra of most materials are highly coherent and this could result in confusion in the mixture estimation. In this work we present a novel approach for spectral dictionary coherence reduction and discuss the feasibility of the methodologies in terms of mutual coherence and approximation error values using overcomplete dictionaries. We compare standard sparse unmixing procedures with our novel derivative method. The presented method was tested on both simulated hyperspectral image as well as on a AVIRIS data

Sparse Pixel-wise spectral Unmixing - which Algorithm to use and how to improve the Results

Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation methods sometimes have problems with selection of correct spectra from the dictionary when these are similar. In this paper we study the detection and approximation accuracy of different sparse approximation methods as well as the influence of the proposed modifications

Simulation of EnMAP data using AVIRIS data

This contribution focuses on mosaicking of airborne data of AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) that is used for various preparatory activities of the spaceborne mission EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) planned to be launched in 2019. Such data can e.g. be used for validating the fully-automatic image processing chain or to provide, in advance and as realistic as possible, example products to future EnMAP users. AVIRIS is selected since it covers a spectral range from 380 nm to 2500 nm which is slightly larger than that of EnMAP (420 nm to 2450 nm). Both sensors exhibit a similar contiguous spectral sampling distance of 10 nm. The dataset consists of 11 stripes covering an area in California/USA and was acquired on April 14 2007 between 17:05 and 22:55 UTC with the sensor to ground distance ranging from approximately 16 km to 20 km, which results in many atmospheric effects similar to the spaceborne data. AVIRIS data have a higher geometric resolution and a swath of only 11 km. In order to obtain EnMAP's geometric parameters with the swath of 30 km and geometric resolution of 30 m × 30 m at least four flight lines have to be combined taking appropriate overlaps and margins into account. To close the gap between AVIRIS and EnMAP data, three steps have to be conducted, (a) a single mosaic has to be generated using the AVIRIS data, (b) the dataset has to be spectrally resampled and (c) a geometric transformation has to be applied simulation of EnMAP data using AVIRIS data (PDF Download Available)

Restoration of EnMAP Data through Sparse Reconstruction

This paper presents the first results of applying sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. Each mage element is independently decomposed using contributions from a limited number of pixels, which come directly from the image and have previously undergone a low-pass filtering in noisy bands. Thus, the denoising application is reduced to a weighted sparse unmixing problem. A first assessment of the results is encouraging as the original bands taken into account are reconstructed with a high Signal-to-Noise Ratio and low overall distortions