Spatial Spectral Endmember Extraction Research Articles

Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.

Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach

Endmember extraction algorithms (EEAs) are among the most commonly discussed types of hyperspectral image processing in the past three decades. This article proposes a spatial energy prior constrained maximum simplex volume (SENMAV) approach for spatial-spectral endmember extraction of hyperspectral images. SENMAV investigates the spatial information from the perspective of the spatial energy prior of a Markov random field (MRF), which is used as a regularization term of the traditional maximum volume simplex model to simultaneously constrain the selection of the endmembers in both the spatial and spectral viewpoints. This article sheds new light on spatial-spectral-based EEAs, as SENMAV well balances the tradeoff between endmember extraction accuracy and spatial attribute requirements of endmembers. Based on the spectral angle distance and root-mean-square error, experimental results on both synthetic and real hyperspectral datasets indicate that the proposed approach significantly improves the endmember extraction performance over current state-of-the-art spatial-spectral-based EEAs.

Parallel Implementation of Spatial–Spectral Endmember Extraction on Graphic Processing Units

The identification of pure spectral signatures (endmembers) in remotely sensed hyperspectral images has traditionally focused on the spectral information alone. Recently, techniques such as the spatial–spectral endmember extraction (SSEE) have incorporated both the spectral and the spatial information contained in the scene. Since hyperspectral images contain very detailed information in the spatial and spectral domain, the integration of these two sources of information generally comes with a significant increase in computational complexity. In this paper, we develop a new computationally efficient implementation of SSEE using commodity graphics processing units (GPUs). The relevance of GPUs comes from their very low cost, compact size, and the possibility to obtain significant acceleration factors by exploiting properly the GPU hardware architecture. Our experimental results, focused on evaluating the candidate endmembers produced by SSEE and also the computational performance of the GPU implementation, indicated significant acceleration factors that allow exploiting the SSEE method in computationally efficient fashion.

Mapping of NiCu–PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada

This study first investigates using AISA airborne hyperspectral imagery (2m spatial resolution) to produce detailed lithologic maps in a subarctic region (Nunavik, Canada) where ultramafic rock units associated with Ni–Cu-(PGE) mineralization are exposed in the presence of lichen coatings. Twenty AISA flight-lines were radiometrically leveled and merged to form a 10×20km mosaic, which then served to generate a simulated spaceborne EnMAP scene (30m spatial resolution) using the End-to-End Simulation Tool to assess the sensors mapping capabilities. Spatial Spectral Endmember Extraction was used to derive spectral endmembers for the AISA and EnMAP data. Image endmembers were compared with spectral measurements of field samples to assess how well key rock types were represented. Results show that the AISA imagery provided a better representation of mafic and ultramafic rock types compared with the EnMAP simulation. Endmembers were then used to map the distribution of geological materials using Iterative Spectral Mixture Analysis. Results indicate the airborne data provided more detailed maps compared with EnMAP simulated data. However, EnMAP data could still discriminate and map broad scale lithological units, specifically mafic and ultramafic rocks. This study demonstrates the feasibility of EnMAP to provide large scale reconnaissance mapping capability of geologic materials over vast subarctic and arctic regions (potentially 30×5000km of imagery per day) using expert knowledge combined with automated spectral analysis methods.

ENDMEMBER EXTRACTION FOR HYPERSPECTRAL IMAGES USING WATERSHED AND NORMALIZED CUTS

Abstract. Endmember extraction integrated with spatial information has been concerned on some research recently. In this paper we studies an improved endmember extraction method with spatial preprocessing module which use watershed with normalized cuts to avoid over- segmentation and produce accurate results from spectral mixture analysis. The spatial-spectral endmember extraction method which used the advantages of image segmentation generates the preliminary results and provides important knowledge for future research.

Spatial Sub-Sampling Using Local Endmembers for Adapting OSP and SSEE for Large-Scale Hyperspectral Surveys

Airborne and satellite hyperspectral sensors can collect significant quantities of data, commonly terabits in size. Thus, there is a need for the development of computational cost effective algorithms that speed up processing, yet retain essential data quality and information. Endmember extraction is critical in the processing chain of hyperspectral data. Existing methods have primarily been demonstrated on small data sets so their usefulness with large data sets has not been fully explored. The objective of this paper is to adapt the Orthogonal Subspace Projection (OSP) and Spatial Spectral Endmember Extraction (SSEE) algorithms, such that they run efficiently on large data sets without the loss of global image endmember quality. This is demonstrated using a simulated and two real hyperspectral images, the last of which is a multi- flight-line survey. The two adapted methods (OSP SS and SSEE SS) make use of spatial sub-sampling via local endmember extraction to reduce the size of the original data set. This paper will demonstrate that this type of spatial sub-sampling retains the full volume of the data, and thus, the vertices of the simplex that represent the global image endmembers. The results also show that computational time is reduce by more than half using the adapted methods. For the large multi- flight-line survey SSEE SS was able to better retain endmembers of natural materials compared with OSP SS. These results illustrate the potential of spatial sub-sampling for other endmember extraction tools when dealing with multi-flight-line surveys.

Improving Spatial–Spectral Endmember Extraction in the Presence of Anomalous Ground Objects

Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial–spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend to neglect anomalous endmembers that are also of interest. Therefore, in this paper, an improved SSEE (iSSEE) algorithm is proposed to address such limitation of conventional SSEE algorithms by accounting for both anomalous and normal endmembers. By developing simplex projection and simplex complementary projection, all the hyperspectral pixels are projected into a simplex determined by the normal endmembers extracted in conventional SSEE algorithms. As a result, anomalous endmembers are identified iteratively by utilizing the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$l_{2}^{\infty}$</tex></formula> norm to find the maximum simplex complementary projection. In order to determine how many anomalous endmembers are to be extracted, a novel Residual-be-Noise Probability-based algorithm is also proposed by elegantly utilizing the spatial-purity map generated in the previous SSEE step. Experimental results on both synthetic and real datasets demonstrate that simplex projection errors can be significantly reduced by identifying both anomalous and normal endmembers in the proposed iSSEE algorithm. It is also confirmed that the performance of the proposed iSSEE algorithm clearly outperforms that of SEE algorithms since both spatial context and spectral similarity are utilized.

Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing

Linear spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral endmembers from hyperspectral data, with many of them relying exclusively on the spectral information. In this letter, we develop a novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information. The proposed approach can be combined with any spectral-based endmember extraction technique. Our method is validated using both synthetic scenes constructed using fractals and a real hyperspectral data set collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite Mining District in Nevada and further compared with previous efforts in the same direction such as the spatial-spectral endmember extraction, automatic morphological endmember extraction, or SPP methods.

Impact of JPEG2000 compression on endmember extraction and unmixing of remotely sensed hyperspectral data

Lossy hyperspectral image compression has received considerable interest in recent years due to the extremely high dimensionality of the data. However, the impact of lossy compression on spectral unmixing techniques has not been widely studied. These techniques characterize mixed pixels (resulting from insufficient spatial resolution) in terms of a suitable combination of spectrally pure substances (called endmembers) weighted by their estimated fractional abundances. This paper focuses on the impact of JPEG2000-based lossy compression of hyperspectral images on the quality of the endmembers extracted by different algorithms. The three considered algorithms are the orthogonal subspace projection (OSP), which uses only spatial information, and the automatic morphological endmember extraction (AMEE) and spatial spectral endmember extraction (SSEE), which integrate both spatial and spectral information in the search for endmembers. The impact of compression on the resulting abundance estimation based on the endmembers derived by different methods is also substantiated. Experimental results are conducted using a hyperspectral data set collected by NASA Jet Propulsion Laboratory over the Cuprite mining district in Nevada. The experimental results are quantitatively analyzed using reference information available from U.S. Geological Survey, resulting in recommendations to specialists interested in applying endmember extraction and unmixing algorithms to compressed hyperspectral data.

Integration of spatial–spectral information for the improved extraction of endmembers

Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial–spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast endmembers, thus improving the potential for these endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar endmembers, allowing for separation of endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of endmembers. The results show that the SSEE method is an effective approach to extracting image endmembers. Specific improvements include the extraction of physically meaningful, low contrast endmembers that occupy unique image regions.