Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Key Information Retrieval in Hyperspectral Imagery through Spatial-Spectral Data Fusion

Hyperspectral (HS) imaging is measuring the radiance of materials within each pixel area at a large number of contiguous spectral wavelength bands. The key spatial information such as small targets and border lines are hard to be precisely detected from HS data due to the technological constraints. Therefore, the need for image processing techniques is an important field of research in HS remote sensing. A novel semisupervised spatial-spectral data fusion method for resolution enhancement of HS images through maximizing the spatial correlation of the endmembers (signature of pure or purest materials in the scene) using a superresolution mapping (SRM) technique is proposed in this paper. The method adopts a linear mixture model and a fully constrained least squares spectral unmixing algorithm to obtain the endmember abundances (fractional images) of HS images. Then, the extracted endmember distribution maps are fused with the spatial information using a spatial-spectral correlation maximizing model and a learning-based SRM technique to exploit the subpixel level data. The obtained results validate the reliability of the technique for key information retrieval. The proposed method is very efficient and is low in terms of computational cost which makes it favorable for real-time applications

Subpixel Target Enhancement in Hyperspectral Images

Hyperspectral images due to their higher spectral resolution are increasingly being used for various remote sensing applications including information extraction at subpixel level. Typically whenever an object gets spectrally resolved but not spatially, mixed pixels in the images result. Numerous man made and/or natural disparatetar gets may thus occur inside such mixed pixels giving rise to subpixel target detection problem. Various spectral unmixing models such as linear mixture modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding a bundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented. In this method, the subpixel target detection is performed by adjusting spatial distribution of abundance fraction within a pixel of an hyperspectral image. Results obtainedat different resolutions indicate that super-resolution mapping may effectively be utilized in enhancing the target detection at sub-pixel level.Defence Science Journal, 2013, 63(1), pp.63-68, DOI:http://dx.doi.org/10.14429/dsj.63.376

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

スペクトルの線形性を考慮したハイパースペクトラル画像のノイズ除去とアンミキシングに関する研究

This study aims to generalize color line to M-dimensional spectral line feature (M>3) and introduce methods for denoising and unmixing of hyperspectral images based on the spectral linearity.For denoising, we propose a local spectral component decomposition method based on the spectral line. We first calculate the spectral line of an M-channel image, then using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, thus the algorithm needs to denoise only two grayscale images, regardless of the number of channels. For unmixing, we propose an algorithm that exploits the low-rank local abundance by applying the unclear norm to the abundance matrix for local regions of spatial and abundance domains. In optimization problem, the local abundance regularizer is collaborated with the L2, 1 norm and the total variation.北九州市立大

Hydrocarbon quantification using neural networks and deep learning based hyperspectral unmixing

Hydrocarbon (HC) spills are a global issue, which can seriously impact human life and the environment, therefore early identification and remedial measures taken at an early stage are important. Thus, current research efforts aim at remotely quantifying incipient quantities of HC mixed with soils. The increased spectral and spatial resolution of hyperspectral sensors has opened ground-breaking perspectives in many industries including remote inspection of large areas and the environment. The use of subpixel detection algorithms, and in particular the use of the mixture models, has been identified as a future advance that needs to be incorporated in remote sensing. However, there are some challenging tasks since the spectral signatures of the targets of interest may not be immediately available. Moreover, real time processing and analysis is required to support fast decision-making. Progressing in this direction, this thesis pioneers and researches novel methodologies for HC quantification capable of exceeding the limitations of existing systems in terms of reduced cost and processing time with improved accuracy. Therefore the goal of this research is to develop, implement and test different methods for improving HC detection and quantification using spectral unmixing and machine learning. An efficient hybrid switch method employing neural networks and hyperspectral is proposed and investigated. This robust method switches between state of the art hyperspectral unmixing linear and nonlinear models, respectively. This procedure is well suited for the quantification of small quantities of substances within a pixel with high accuracy as the most appropriate model is employed. Central to the proposed approach is a novel method for extracting parameters to characterise the non-linearity of the data. These parameters are fed into a feedforward neural network which decides in a pixel by pixel fashion which model is more suitable. The quantification process is fully automated by applying further classification techniques to the acquired hyperspectral images. A deep learning neural network model is designed for the quantification of HC quantities mixed with soils. A three-term backpropagation algorithm with dropout is proposed to avoid overfitting and reduce the computational complexity of the model. The above methods have been evaluated using classical repository datasets from the literature and a laboratory controlled dataset. For that, an experimental procedure has been designed to produce a labelled dataset. The data was obtained by mixing and homogenizing different soil types with HC substances, respectively and measuring the reflectance with a hyperspectral sensor. Findings from the research study reveal that the two proposed models have high performance, they are suitable for the detection and quantification of HC mixed with soils, and surpass existing methods. Improvements in sensitivity, accuracy, computational time are achieved. Thus, the proposed approaches can be used to detect HC spills at an early stage in order to mitigate significant pollution from the spill areas

Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures

Hyperspectral image (HSI) has been widely adopted in many real-world applications due to its potential to provide detailed information from spectral and spatial data in each pixel. However, precise classification of an object from HSI is challenging due to complex and highly correlated features that exhibit a nonlinear relationship between the acquired spectral unique to the HSI object. In literature, many research works have been conducted to address this problem. However, the problem of processing high-dimensional data and achieving the best resolution factor for any set of regions remains to be evolved with a suitable strategy. Therefore, the proposed study introduces simplified modeling of the hyperspectral image in which precise detection of regions is carried out based on the characterization of pure signatures based on the estimation of the maximum pixel mixing ratio. Moreover, the proposed system emphasizes the pixel unmixing problem, where input data is processed concerning wavelength computation, feature extraction, and hypercube construction. Further, a non-iterative matrix-based operation with a linear square method is performed to classify the region from the input hyperspectral image. The simulation outcome exhibits efficient and precise object classification is achieved by the proposed system in terms classified HSI object and processing time

An Image fusion algorithm for spatially enhancing spectral mixture maps

An image fusion algorithm, based upon spectral mixture analysis, is presented. The algorithm combines low spatial resolution multi/hyperspectral data with high spatial resolution sharpening image(s) to create high resolution material maps. Spectral (un)mixing estimates the percentage of each material (called endmembers) within each low resolution pixel. The outputs of unmixing are endmember fraction images (material maps) at the spatial resolution of the multispectral system. This research includes developing an improved unmixing algorithm based upon stepwise regression. In the second stage of the process, the unmixing solution is sharpened with data from another sensor to generate high resolution material maps. Sharpening is implemented as a nonlinear optimization using the same type of model as unmixing. Quantifiable results are obtained through the use of synthetically generated imagery. Without synthetic images, a large amount of ground truth would be required in order to measure the accuracy of the material maps. Multiple band sharpening is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. The analysis includes an examination of the effects of constraints and texture variation on the material maps. The results show stepwise unmixing is an improvement over traditional unmixing algorithms. The results also indicate sharpening improves the material maps. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map