Extracting pure endmembers using symmetric sparse representation for hyperspectral imagery

The hyperspectral/spatial detection of edges (HySPADE) algorithm, originally published in 2004 [1], has been modified and applied to a wider diversity of hyperspectral imagery (HSI) data. As originally described in [1], HySPADE operates by converting the naturally two-dimensional edge detection process based on traditional image analysis methods into a series of one-dimensional edge detections based on spectral angle. The HySPADE algorithm: i) utilizes spectral signature information to identify edges; ii) requires only the spectral information of the HSI scene data and does not require a spectral library nor spectral matching against a library; iii) facilitates simultaneous use of all spectral information; iv) does not require endmember or training data selection; v) generates multiple, independent data points for statistical analysis of detected edges; vi) is robust in the presence of noise; and vii) may be applied to radiance, reflectance, and emissivity data--though it is applied to radiance and reflectance spectra (and their principal components transformation) in this report. HySPADE has recently been modified to use Euclidean distance values as an alternative to spectral angle. It has also been modified to use an N x N-pixel sliding window in contrast to the 2004 version which operated only on spatial subset image chips. HySPADE results are compared to those obtained using traditional (Roberts and Sobel) edge-detection methods. Spectral angle and Euclidean distance HySPADE results are superior to those obtained using the traditional edge detection methods; the best results are obtained by applying HySPADE to the first few, information-containing bands of principal components transformed data (both radiance and reflectance). However, in practice, both the Euclidean distance and spectral angle versions of HySPADE should be applied and their results compared. HySPADE results are shown; extensions of the HySPADE concept are discussed as are applications for HySPADE in HSI analysis and exploitation.

Inspired by the recent rapid progress of l1-norm minimization techniques and the great success of sparse dictionary learning in image modeling, this paper proposes a sparse multi-way models clustering fusion technique to improve the classification performance in hyperspectral imagery. Multi-way models consider hyperspectral imagery data as a whole entity to treat jointly spatial and spectral modes. The whole clustering fusion method is composed three steps. Firstly, the complete hyperspectral data is grouped into several independent sub-band data sources. Then, sparse multi-way model is used to feature extraction in every band set, and divide the scene into a series of homomorphic regions. At last, we propose a fusion method to combine the information provided by each band set, it can acquire approximate supervised classification performance (such as K-nearest Neighbor classifier).The experimental results on the HYDICE imagery demonstrate the efficiency and superiority of the proposed clustering method to the classical K-means clustering method.

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium or long distance by an airbone or satellite sensor. Over the last few years, hyperspectral image data sets have been collected for a great amount of locations over the world, using a variety of instruments for Earth observation. Despite the increasing importance of hyperspectral images in remote sensing applications, there is no common repository of hyperspectral data intended to distribute and share hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available for public use are spread among different storage locations and present significant heterogeneity regarding the storage format, associated meta-data (if any), or ground-truth availability. As a result, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we take a necessary first step towards the development of a digital repository for remotely sensed hyperspectral data. The proposed system allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results, with the ultimate goal of sharing publicly available hyperspectral images within the remote sensing community. The database has been designed in order to allow storing relevant information for the hyperspectral data available through the system, including basic image characteristics (width, height, number of bands, format) and more advanced meta-data (ground-truth information, publications in which the data has been used). The current implementation consists of a front-end to ease the management of images through a web interface, thus containing both synthetic and real hyperspectral images from two highly representative instruments, such as NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining District in Nevada. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images on the spectral unmixing information (spectrally pure components or endmembers and their associated abundances in the scene). This information is stored as meta-data associated to each hyperspectral image instance, and then used to search and retrieve images based on information content. This paper presents the design of the system and a preliminary validation of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images stored in the database.

: This one-year effort will focus on the transition of FERI's machine learning algorithms for HyperSpectral Imagery (HSI) in the VSW into a distributable code set. This will provide a stable code platform for the application and transition of machine learning-based hyperspectral classification techniques into 6.3/6.4 programs. (This work was funded mid-year 2008.) Our objective is to focus on three areas of application research and transitions. First, we will transition our machine learning-based algorithms and computer code for the determination of bathymetry, bottom type, and water column Inherent Optical Properties from HyperSpectral Imagery (HSI) into a deliverable Message Passing Interface (MPI) program that may be easily used by other research and military operators. Second, we will use this program to determine the impacts of the granularity of the classification database on the inversion bathymetry, bottom type, and IOPs. Third, we will move beyond the use of single pixel HSI inversion to the use of spatial context-filtering to remove pixel-topixel noise inherent in the HSI data.

The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.

Spectral mixture analysis (also called spectral unmixing) is one of the important and effective techniques to estimate abundance fractions of materials present in the hyperspectral imagery. Linear spectral mixture modeling is widely used in solving the spectral unmixing problems as its conciseness and clarity of physical meaning. This paper presents a novel algorithm to produce fully constrained (i.e. non-negative and sum to one constrained) abundance using the barycentric coordinates in the n-simplex. To impose non-negative constraint on the abundance, the proposed method use a serious of orthogonal projections to find the fully constrained solution, which takes into account the geometric structure of hyperspectral data set. The proposed algorithm is in line with the least squares criterion. The efficiency and effectiveness of the resulting unmixing algorithm is demonstrated using both synthetic and real hyperspectral images.

Hyperspectral Imagery (HSI) data are inherently more expensive and difficult to acquire than Multispectral Imagery (MSI) data. Most HSI acquisitions are currently limited to relatively low spatial resolution with the inherent limitation that they are not able to spatially resolve many important targets. Higher spatial resolution MSI data are available, however, they typically don’t provide sufficient spectral resolution to allow unambiguous identification of specific targets. We describe a new method using predictive modeling of combined HSI/MSI data to generate simulated high-spatial- resolution, high-spectral-resolution image data. Targets observed by the low spatial resolution HSI sensor are spectrally resolved through linear spectral unmixing, but not spatially resolved. Targets observed by higher spatial resolution MSI sensor are partially spatially resolved but lower spectral resolution doesn’t allow target identification. The fused data are used to predict the subpixel locations of HSI-identified subpixel targets within the superior MSI spatial resolution. Comparison of individual HSI endmember spectra to MSI signatures at multiple MSI pixels allows mapping the spatial locations of specific HSI endmembers creating a simulated HSI dataset that allows location of spectrally resolved targets. The combined data incorporate the best features of HSI/MSI to allow improved target detection and characterization.

The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data is widely used for land cover classification. However, due to different imaging mechanisms, HSI and LiDAR data always present significant image differences, and the dimensions and feature distributions of HSI and LiDAR are highly dissimilar. This makes it challenging to represent and correlate semantic information from multimodal data. Current methods for classifying pixel-by-pixel features, which rely on cascaded or attention-based fusion, cannot effectively use multimodal features. To achieve accurate classification results, extracting and fusing similar high-order semantic information and complementary discriminative information contained in multimodal data is vital. In this paper, we propose a cross-modal semantic enhancement network (CMSE) for multimodal semantic information mining and fusion. Our proposed CMSE framework extracts features from the image on multiple scales, capturing more representative local sparse features with different sizes of convolution kernels. To represent high-level semantic features related to land cover, we establish a Gaussian-weighted matrix and semantically transform the spatial and spectral features of distinct branches. Finally, we build a multi-level residual fusion module to incrementally fuse spectral features from HSI and elevation features from LiDAR. Additionally, we introduce a cross-modal semantically constrained loss to guide multimodal semantic feature alignment. We evaluate our approach on three multimodal remote sensing datasets, namely the Houston2013, Trento, and MUUFL datasets. The experimental results demonstrate that our proposed CMSE model achieves superior performance in terms of accuracy and robustness compared to other related deep networks.

Independent Component Analysis (ICA) is a blind source separation method which is exploited for various applications in signal processing. In hyperspectral imagery, ICA is commonly employed for detection and segmentation purposes. But it is often thought to be unable to quantify abundances. In this paper, we propose an ICA-based method to estimate the anomaly abundances from the independent components. The first experiments on synthetic and real world hyperspectral images are very promising referring to the estimation accuracy and robustness.

Due to the data-hungry nature of deep neural networks and the high dimensionality of hyperspectral imagery (HSI) data, the scarcity of training samples remains a significant challenge for HSI classification based on deep learning. Recently, the pairing or recombining of samples has proven to be an efficient approach to increase the amount of input training data, and accordingly, the architecture of a network trained by these samples has to be redesigned, since the input is no longer a single sample. Following this strategy, in this article, we propose a spatial–spectral relation network (SS-RN) for HSI classification with limited training samples. The SS-RN takes advantage of the relation network architecture to precisely capture the profound similarity between samples. Additionally, to make SS-RN more suitable for HSI classification, an entire three-dimensional (3-D) neighborhood instead of the isolated pixel is considered to explore both spectral and spatial information thoroughly. Multiple support samples are also selected for each class to make sure that the extracted features are stable enough to avoid the intraclass similarity and interclass dissimilarity problem of HSI. The network is composed on the basis of 3-D convolutional neural network blocks with the aim of extracting spatial–spectral features. Extensive experiments on three widely used HSI datasets demonstrate that the proposed method can achieve better classification accuracy than conventional deep learning methods with limited labeled training samples.

Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery

Abstract. In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of correlation clustering lies in its ability to simultaneously perform feature reduction and clustering. This algorithm also allows selection of different sets of features for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. ORCLUS, a correlation clustering algorithm, is implemented and enhanced by making use of segmented principal component analysis (SPCA) instead of principal component analysis (PCA). Further, original implementation of ORCLUS makes use of eigenvectors corresponding to smallest eigenvalues whereas in this study eigenvectors corresponding to maximum eigenvalues are used, as traditionally done when PCA is used as feature reduction tool. Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.

In order to overcome low detection accuracy and high false alarm rate of anomaly target detection based on Kernel RX anomaly detector in hyperspectral imagery (HSI). The paper proposes a new HSI anomaly target detection algorithm based on optimal bands subspace that analyses the characteristics of HSI data. First, the wavelet transform method is used to denoise HSI, which makes that the background information of hyperspectral imagery is suppressed by using the advantages of wavelet transform. Then, HSI is divided for band subsets by the classical adaptive subspace method. The optimal bands are selected by using the minimum variance index in each band subsets. Finally, the Kernel RX algorithm is used to detect anomaly target from optimal band subspaces. The data sets of real AVIRIS and synthetic HSI are used in the experiments, and the results show that the proposed algorithm has strong robustness and lower false alarm probability.

Traditional soil nutrient and moisture analysis is labor-intensive, costly, and lacks spatial granularity, hindering precision agriculture. This paper proposes an integrated machine learning pipeline that fuses hyperspectral imagery (HSI) a powerful environmental sensing technology with advanced computer vision techniques to address this gap. We hypothesize that deep learning models can decode the complex, non-linear spectral signatures in HSI data to predict key soil properties accurately. Our methodology encompasses HSI data preprocessing, feature extraction using a Convolutional Neural Network (CNN), and regression modeling. Using a public dataset, we demonstrate that our proposed CNN-based model outperforms traditional spectral indices and machine learning models like Support Vector Regression (SVR) in predicting soil organic carbon (SOC) and moisture content. The results indicate the high potential of this pipeline for generating high-resolution, actionable soil maps to optimize resource use in agriculture.