Analysis Of Hyperspectral Imagery Research Articles

FPGA-based remote target classification in hyperspectral imaging using multi-graph neural network

Hyperspectral imagery (HSI) is widely used in remote sensing for target classification; however, its accurate classification remains challenging due to the scarcity of labeled data. Graph Neural Networks (GNNs) have emerged as a popular method for semi-supervised classification, attracting significant interest in the context of HSI analysis. Nevertheless, conventional GNN-based approaches often rely on a single graph filter to extract HSI characteristics, failing to fully exploit the potential benefits of different graph filters. Additionally, oversmoothing issues plague classical GNNs, further affecting classification performance. To address these drawbacks, we propose a novel approach called Spectral and Autoregressive Moving Average Graph Filter for the Multi-Graph Neural Network (SAM-GNN). This approach leverages two distinct graph filters: one specialized in extracting the spectral characteristics of nodes and the other effectively suppressing graph distortion. Through extensive evaluations, we compare the performance of SAM-GNN with other state-of-the-art methods, employing metrics such as overall accuracy (OA), individual class accuracy (IA), and Kappa coefficient (KC). The results shows that the SAM-GNN provides an improvement in KC, IA, and OA of 6.71%, 5.7%, and 3.93% for the Pavia University dataset and 4.67%, 3.67%, and 3.49% for the Cuprite dataset respectively. Furthermore, we implement SAM-GNN on the Virtex-7 field-programmable gate array (FPGA), demonstrating that the method achieves highly accurate target localization results, bringing us closer to real-world applications in HSI classification.

Assessment of the utility of underwater hyperspectral imaging for surveying and monitoring coral reef ecosystems

Technological innovations that improve the speed, scale, reproducibility, and accuracy of monitoring surveys will allow for a better understanding of the global decline in tropical reef health. The DiveRay, a diver-operated hyperspectral imager, and a complementary machine learning pipeline to automate the analysis of hyperspectral imagery were developed for this purpose. To evaluate the use of a hyperspectral imager underwater, the automated classification of benthic taxa in reef communities was tested. Eight reefs in Guam were surveyed and two approaches for benthic classification were employed: high taxonomic resolution categories and broad benthic categories. The results from the DiveRay surveys were validated against data from concurrently conducted photoquadrat surveys to determine their accuracy and utility as a proxy for reef surveys. The high taxonomic resolution classifications did not reliably predict benthic communities when compared to those obtained by standard photoquadrat analysis. At the level of broad benthic categories, however, the hyperspectral results were comparable to those of the photoquadrat analysis. This was particularly true when estimating scleractinian coral cover, which was accurately predicted for six out of the eight sites. The annotation libraries generated for this study were insufficient to train the model to fully account for the high biodiversity on Guam’s reefs. As such, prediction accuracy is expected to improve with additional surveying and image annotation. This study is the first to directly compare the results from underwater hyperspectral scanning with those from traditional photoquadrat survey techniques across multiple sites with two levels of identification resolution and different degrees of certainty. Our findings show that dependent on a well-annotated library, underwater hyperspectral imaging can be used to quickly, repeatedly, and accurately monitor and map dynamic benthic communities on tropical reefs using broad benthic categories.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Background and objectivesCompared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. MethodsTo fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. ResultsThe proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. ConclusionsThe proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis. However, the influence of light, acquisition conditions and the inherent properties of materials, results in that the identified endmembers can vary spectrally within a given image (construed as spectral variability). To address this issue, recent methods usually use a priori obtained spectral library to represent multiple characteristic spectra of the same object, but few of them extracted the spectral variability explicitly. In this paper, a spectral variability augmented sparse unmixing model (SVASU) is proposed, in which the spectral variability is extracted for the first time. The variable spectra are divided into two parts of intrinsic spectrum and spectral variability for spectral reconstruction, and modeled synchronously in the SU model adding the regular terms restricting the sparsity of abundance and the generalization of the variability coefficient. It is noted that the spectral variability library and the intrinsic spectral library are all constructed from the In-situ observed image. Experimental results over both synthetic and real-world data sets demonstrate that the augmented decomposition by spectral variability significantly improves the unmixing performance than the decomposition only by spectral library, as well as compared to state-of-the-art algorithms.

A Probability Metric-Based Autoencoder for Hyperspectral Unmixing

In recent years, hyperspectral unmixing has been a crucial preprocess in hyperspectral imagery analysis. Current learning-based methods of hyperspectral unmixing are built on the autoencoder framework. Most of them hold a point-wise loss function only comparing the individual difference without the overall difference. As a remedy, we propose a probability metric based autoencoder (PMAE) for hyperspectral unmixing. Specifically, we combine a distribution distance called maximum mean discrepancy (MMD) with underlying loss spectral angle distance (SAD) as the final loss function for training the unmixing autoencoder. The MMD can be seen as a regularization that is designed to match all orders of statistics between the input spectrums and the reconstructed spectrums. The SAD is to compare the individual difference while the MMD compares the overall difference. Combing these two points, we can obtain more prior information to guide the autoencoder training towards boosting the unmixing performance. In order to validate this viewpoint, we conduct model analysis and a series of ablation studies showing that adding the MMD into the autoencoder unmixing model indeed boosts the unmixing performance a lot compared to the single SAD objective. Experimental results on five real hyperspectral scenes demonstrate the effectiveness and competitiveness of the proposed PMAE compared with several classical and learning-based approaches.

Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field

Although Bayesian methods have been very effective for spatial-spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced sub-pixel mapping (SPM) by simultaneously considering several key issues i.e., endmember variability, the discrete nature of subpixel class labels and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember-abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels while taking into account the noise heterogeneity effect. Second, the discrete class label field modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation-maximization (EM) iteration, where E-step estimates the subpixel class label field using a simulated annealing (SA) algorithm and M-step estimates the endmembers for each pixel in HSI using alternating nonnegative least squares (ANLS) approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Collaborative and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery

Sparse representation can be used for the representation of high-dimensional data into a low-dimensional subspace. Recently, sparse graph-based discriminant analysis that uses l 1-norm optimization has drawn much attention in dimensionality reduction of hyperspectral imagery. By combining low-rankness and sparsity, sparse and low-rank representation based discriminant analysis (SLGDA) can effectively capture global and local data structures simultaneously. In this article, collaborative and low-rank representation based discriminant analysis (CLGDA) is proposed, which is different from the concept of sparse representation. The more informative graph can be obtained in CLGDA with the combination of both collaborative representation (CR) and low-rank representation (LRR) because global data structure can be preserved by LRR and collaboration among within-class atoms is important than competition in sparse representation. Moreover, CR with l 2-norm regularization is computationally efficient and competitive to sparse representation. The experimental results conducted on three hyperspectral datasets demonstrate that the proposed CLGDA and its variants can provide better classification performance in comparison to SLGDA counterparts with lower computational complexity.

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.

A real-time FPGA accelerated stream processing for hyperspectral image classification

Realizing real-time classification of hyperspectral imagery has attracted researchers across various scientific and engineering disciplines. Several time-critical applications of high-resolution hyperspectral imaging require analysis of hyperspectral imagery with minimal turnaround time. In this regard, field-programmable gate arrays (FPGAs) provide cost-effective performance over the other high-performance reconfigurable computing systems. Developing such efficient systems poses several technical challenges such as designing and validating the FPGA architecture. A rapid prototyping approach offers several benefits in designing and developing FPGA architectures. This paper proposes a rapid prototyping method for the design and implementation of a multi-class support vector machine (MSVM) algorithm for real-time hyperspectral imagery classification using a low-cost FPGA system. We have evaluated design performance in terms of overall accuracy, resource utilization as well as timing requirements using four different datasets containing airborne, drone, and ground-based hyperspectral imagery. Experimental results show that the proposed FPGA design performs classification under strict real-time constraints.

Adaptive collaborative graph for discriminant analysis of hyperspectral imagery

ABSTRACT Recently, sparse graph-based discriminant analysis (SGDA) and collaborative graph-based discriminant analysis (CGDA) have been developed for dimensionality reduction of hyperspectral imagery. In SGDA or CGDA, a graph is constructed by -norm minimization-based representation or -norm minimization-based representation, respectively, based on labeled samples. These two methods have shown success over traditional methods by reinforcing discriminative power, but are limited in within-class representation and distribution of representation coefficients. In order to preserve intrinsic geometrical structure of original data and improve the interpretability of the underlying graph, we propose an adaptive collaborative graph for discriminant analysis (ACGDA), which explicitly models the internal relationship among within-class pixels during graph construction. The proposed method couples distance-weighted Tikhonov regularization with -norm minimization-based representation, of which the coefficients are solutions to a closed-form expression. The graph adaptively adjusts the collaborative representation by using distance-weighted measurement, which produces stronger ability of discrimination. In addition, the graph weight matrix is designed in the form of a block-diagonal structure, reducing the computational cost and further improving discriminative power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets. Experimental results demonstrate that the proposed approach can yield superior classification performance.

Incorporating band selection in the spatial selection of spectral endmembers

The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction (SSEE) tool to determine a band set that can be used to better define endmembers classes used in spectral mixture analysis. The incorporation aims to define a band set that improves the spectral contrast between endmembers at each step of the spatial-spectral endmember search and ultimately captures key features for discriminating spectrally similar materials. The proposed method (NSSA-SSEE) was evaluated for lithological mapping using a hyperspectral image encompassing a range of spectrally similar mafic and ultramafic rock units. The band selected by NSSA-SSEE showed a good agreement with known features of scene components identified by experts. Results showed an improvement in the selection of detailed endmembers, endmembers that are similar and that can be significant for mapping. The incorporation of NSSA into SSEE was feasible because both methods are well suited for this process. NSSA is one of the few methods of band selection that is suitable for the analysis of a small number of endmembers and SSEE provides such endmember sets via spatial subsetting. The automated NSSA-SSEE approach can reduce the need for field-based information to guide the feature selection process.

Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery

Dimensionality Reduction (DR) models are of significance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we firstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but efficient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs efficiently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.

NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE

Abstract. Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

Spatially Adaptive Sparse Representation for Target Detection in Hyperspectral Images

As sparse representation gradually obtains better and better results in the analysis of hyperspectral imagery and sparsity-based algorithms are becoming more and more popular, especially in target detection. However, these methods mostly assume an absolute equal contribution by all neighboring pixels while detecting the central pixel. There is no doubt that this approach is unsuitable for pixels located in heterogeneous areas. In this letter, to address this problem, spatially adaptive sparse representation for target detection in hyperspectral images (HSIs) is proposed. Neighboring spatial information is utilized by considering the different contributions of the distinct neighborhood pixels. The different weights are determined according to the similarity between the neighboring pixels and the central test pixel. The proposed algorithm was tested on two HSIs and demonstrated outstanding detection performance when compared with other commonly used detectors.

Reflectance Spectroscopy and Hyperspectral Imaging of Sapphire-Bearing Marble From the Beluga Occurrence, Baffin Island, Nunavut

Abstract Baffin Island (Nunavut, Canada) is located in the remote arctic where geological exposure is high, terrain is expansive, and field seasons are short. Metacarbonates in the Lake Harbour Group, southern Baffin Island, are considered to be prospective for sapphire (corundum), lapis lazuli (lazurite), and spinel gem deposits. This research is an investigation of the hyperspectral signature of rocks from the Beluga sapphire occurrence near Kimmirut, Nunavut. Thirty-five rock and thin section offcut samples from the Beluga corundum occurrence were studied in a laboratory setting using high spatial resolution imaging spectroscopy in the shortwave infrared region (SWIR, ∼975 to 2500 nm). Analysis of hyperspectral imagery successfully reproduced mineralogical and textural information relevant to gem mineralization previously assessed through thin section petrography and scanning electron microscopy. Scapolite, phlogopite, and muscovite from these rocks are shown to have distinct spectral responses from the host marble that is dominated by calcite. Furthermore, spectrally distinct prehnite and zeolite, both locally associated with corundum mineralization, are readily distinguishable. From the perspective of regional exploration, the small footprint of colored gemstone deposits in combination with their unique mineralogical makeup, unusual settings, and unfamiliarity to most geoscientists has resulted in very few discoveries in this remote but otherwise fertile area. These factors, in combination with the unique spectral responses observed from hand samples and thin section offcuts, provide a positive case for mapping and exploration of gem corundum targets by high spatial resolution hyperspectral imaging as part of regional surveys.

An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation

With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Laplacian Regularized Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery

Collaborative graph-based discriminant analysis (CGDA) has been recently proposed for dimensionality reduction and classification of hyperspectral imagery, offering superior performance. In CGDA, a graph is constructed by $\ell_{2}$ - norm minimization-based representation using available labeled samples. Different from sparse graph-based discriminant analysis (SGDA) where a graph is built by $\ell_{1}$ - norm minimization, CGDA benefits from within-class sample collaboration and computational efficiency. However, CGDA does not consider data manifold structure reflecting geometric information. To improve CGDA in this regard, a Laplacian regularized CGDA (LapCGDA) framework is proposed, where a Laplacian graph of data manifold is incorporated into the CGDA. By taking advantage of the graph regularizer, the proposed method not only can offer collaborative representation but also can exploit the intrinsic geometric information. Moreover, both CGDA and LapCGDA are extended into kernel versions to further improve the performance. Experimental results on several different multiple-class hyperspectral classification tasks demonstrate the effectiveness of the proposed LapCGDA.

Hyperspectral face recognition with log‐polar Fourier features and collaborative representation based voting classifiers

Hyperspectral imagery analysis has become a popular topic for improving face recognition accuracy. Nevertheless, it encounters difficulty in data acquisition, low signal-to-noise ratio, and high dimensionality. As a result, there exists a need to develop better algorithms in order to achieve higher classification rates. In this study, the authors propose a new method for hyperspectral face recognition with very competitive experimental results. Since there is a significant amount of noise in every spectral band, they reduce noise adaptively from each spectral band by using any image denoising method, e.g. block matching and 3D filtering. They then crop each face according to its eye coordinates so that translation invariance can be achieved. They conduct log-polar transform to each cropped face image and extract 2D Fourier spectrum from them. In this way, the extracted features are approximately invariant to translation, rotation, and scaling. They use the collaborative representation-based classifier with voting for hyperspectral face recognition. They perform some experiments to test the authors’ new method for hyperspectral face recognition with very promising results.

Feature-enhanced spectral similarity measure for the analysis of hyperspectral imagery

In hyperspectral remote sensing, the surface compositional material can be identified by means of spectral matching algorithms. In many cases, the importance of each spectral band to measure spectral similarity is different, whereas the traditional spectral matching algorithms implicitly assume all wavelength-dependent absorption features are equal. This may yield an unsatisfactory performance for spectral matching. To remedy this deficiency, we propose methods called feature-enhanced spectral similarity measures. They are hybrids of the spectral matching algorithms combined with a feature-enhanced space projection, termed feature-enhanced spectral angle measure, feature-enhanced Euclidean distance measure, feature-enhanced spectral correlation measure, and feature-enhanced spectral information divergence. The proposed methods creatively project the original spectra into spectral feature-enhanced space, in which important features for measuring the spectral similarity will be increased to a high degree, whereas features of low importance will be suppressed. In order to demonstrate the effectiveness of the proposed approaches, performances are compared on real hyperspectral image data from Airborne Visible Infrared Imaging Spectrometer. The proposed methods are found to possess significant improvements over the original four spectral matching algorithms.