Hyperspectral Analysis Research Articles

The DIVER Microscope for Imaging in Scattering Media.

We describe an advanced DIVER (Deep Imaging Via Emission Recovery) detection system for two-photon fluorescence microscopy that allows imaging in multiple scattering media, including biological tissues, up to a depth of a few mm with micron resolution. This detection system is more sensitive to low level light signals than conventional epi-detection used in two-photon fluorescence microscopes. The DIVER detector efficiently collects scattered emission photons from a wide area of turbid samples at almost any entrance angle in a 2π spherical angle. Using an epi-detection scheme only photons coming from a relatively small area of a sample and at narrow acceptance angle can be detected. The transmission geometry of the DIVER imaging system makes it exceptionally suitable for Second and Third Harmonic Generation (SHG, THG) signal detection. It also has in-depth fluorescence lifetime imaging (FLIM) capability. Using special optical filters with sin-cos spectral response, hyperspectral analysis of images acquired in-depth in scattering media can be performed. The system was successfully employed in imaging of various biological tissues. The DIVER detector can be plugged into a standard microscope stage and used as an external detector with upright commercial two-photon microscopes.

Hyperspectral analysis of objects under shadow conditions based on field reflectance measurements.

Shaded objects in remote sensing data are usually neglected because of their low reflectance. The common treatment for detecting shadows consists of simple identification and straightforward removal; however, this approach is often criticized owing to the complexity and difficulty of its application and the incompleteness of the treated data. Recent efforts with hyperspectral field reflectance measurements have provided alternatives to scrutinize the issues of shadows. To demonstrate our proposed framework, we first gathered basic hyperspectral data with a spectrometer at Peking University, Beijing, China. Subsequently, we classified the spectral reflectances into three types: sunlight reflectance, shadow reflectance, and simulated reflectance. The spectral characteristics of shadows can provide pertinent classification information despite their low reflectance. Under full shadow conditions, the simulated reflectance was close to the sunlight reflectance because the same reduction ratio was used for the spectralon panel during the measurement process. In addition, the ratio of the shadow reflectance to the sunlight reflectance seemed to be related to the characteristics of the obstruction. The findings in this paper can contribute to a comprehensive understanding of the influence of shadows in remote sensing research, demonstrating the potential of fully utilizing typically neglected shadow information.

ROHSA: Regularized Optimization for Hyper-Spectral Analysis

Context. Extracting the multiphase structure of the neutral interstellar medium is key to understanding star formation in galaxies. The radiative condensation of the diffuse warm neutral medium producing a thermally unstable lukewarm medium and a dense cold medium is closely related to the initial step leading the atomic-to-molecular (HI-to-H2) transition and the formation of molecular clouds. Up to now, the mapping of these phases out of 21 cm emission hyper-spectral cubes has remained elusive mostly due to the velocity blending of individual cold structures present on a given line of sight. As a result, most of the current knowledge about the HI phases rests on a small number of absorption measurements on lines of sight crossing radio sources. Aims. The goal of this work is to develop a new algorithm to perform separation of diffuse sources in hyper-spectral data. Specifically the algorithm was designed in order to address the velocity blending problem by taking advantage of the spatial coherence of the individual sources. The main scientific driver of this effort was to extract the multiphase structure of the HI from 21 cm line emission only, providing a means to map each phase separately, but the algorithm developed here should be generic enough to extract diffuse structures in any hyper-spectral cube. Methods. We developed a new Gaussian decomposition algorithm named ROHSA based on a multi-resolution process from coarse to fine grid. ROHSA uses a regularized nonlinear least-square criterion to take into account the spatial coherence of the emission and the multiphase nature of the gas simultaneously. In order to obtain a solution with spatially smooth parameters, the optimization is performed on the whole data cube at once. The performances of ROHSA were tested on a synthetic observation computed from numerical simulations of thermally bi-stable turbulence. We apply ROHSA to a 21 cm observation of a region of high Galactic latitude from the GHIGLS survey and present our findings. Results. The evaluation of ROHSA on synthetic 21 cm observations shows that it is able to recover the multiphase nature of the HI. For each phase, the power spectra of the column density and centroid velocity are well recovered. More generally, this test reveals that a Gaussian decomposition of HI emission is able to recover physically meaningful information about the underlying three-dimensional fields (density, velocity, and temperature). The application on a real 21 cm observation of a field of high Galactic latitude produces a picture of the multiphase HI, with isolated, filamentary, and narrow (σ ~ 1−2 km s−1) structures, and broader (σ ~ 4−10 km s−1), diffuse, and space-filling components. The test-case field used here contains significant intermediate-velocity clouds that were well mapped out by the algorithm. As ROHSA is designed to extract spatially coherent components, it performs well at projecting out the noise. Conclusions. In this paper we introduce ROHSA, a new algorithm that performs a separation of diffuse sources in hyper-spectral data on the basis of a Gaussian decomposition. The algorithm makes no assumption about the nature of the sources, except that each one has a similar line width. The tests we made shows that ROHSA is well suited to decomposing complex 21 cm line emission of regions of high Galactic latitude, but its design is general enough that it could be applied to any hyper-spectral data type for which a Gaussian model is relevant.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality

BackgroundThe advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.ResultsThe results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds.ConclusionThis study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.

Ordered Structures in Liquid Water as Studied by Raman Spectroscopy and the Phonon Confinement Model

Abstract Raman spectroscopy and the phonon confinement model (PCM) have been used to study the two ordered structures in liquid water, “structured hydrogen bonded water” and “nano-ice”, whose existence has recently been confirmed by a hyperspectral analysis of 140 temperature dependent Raman spectra (−23 to 45 °C). The PCM spectral simulation based on the ice Ih structure indicates that vibrations are limited within a short range of few molecules in “structured hydrogen bonded water”, while they extend to about a 2–4 nm range in “Nano-ice”.

HYPERSPECTRAL ANALYSIS OF ASTER AND LANDSAT 8 DATA TO CHARACTERIZE EL-MISSIKAT URANIUM BEARING SILICEOUS VEINS AND THEIR ANALOGY TO EL-ERADIYA AND GATTAR GRANITES, EASTERN DESERT, EGYPT

This work aims to apply spectral image processing techniques that have been used often to processhyperspectral data to analyze multispectral ASTER (Advanced Spaceborne Thermal Emission andReflection Radiometer) and Landsat 8 data for mapping uranium-bearing siliceous rocks within El-Missikat granite. These techniques succeeded effectively in recognition of the same features at El-Eradiyaand Gattar granites. Siliceous materials (siliceous sinter), as used here, is a spectral group of SiO2 mineralsincluding opal, chalcedony and cristobalite derived from hydrothermal fluids and/or the decomposition ofgranite. The spectral identification of such minerals depend mainly on the uses a hydration absorptionfeature that characterize some hydrated silica at wavelength region of 2.2- to 2.4-μm.The effectiveness of the hyperspectral analysis techniques used to compare a pixel spectrum with thespectra of known pure materials, extracted from the spectral end member selection procedures, includingminimum noise fraction (MNF), pixel purity index (PPI) and n-dimensional visualization. Among thoseof the spectral analysis algorithms employed, spectral angle mapping (SAM) and matched filtering (MF)produced accurate classifications that were close to the ground reference data. The hyperspectralanalysis of an ASTER and Landsat 8 dataset covering the studied areas, has successfully effective indetecting lithological units than traditional multispectral analysis procedures.Field validation and laboratory investigation were carried out to prove and support the resultsobtained from ASTER and Landsat 8 processing. These results succeeded to delineate additional sitesof siliceous materials that could be a promising areas to host the uranium mineralization.

Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field.

Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN)

Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Mapping Pure Mangrove Patches in Small Corridors and Sandbanks Using Airborne Hyperspectral Imagery

Taijiang National Park (TNP) of Taiwan is the northernmost geographical position of mangrove habitats in the Northern Hemisphere. Instead of occupying a vast region with a single species, the mangroves in TNP are usually mingled with other plants in a narrow corridor along the water or in groups on a small sandbank. The multi-spectral images acquired from the spaceborne platforms are therefore limited in mapping the abundance and distribution of the mangrove species in TNP. We report the work of mapping pure mangrove patches in small corridors and sandbanks in TNP using airborne Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery. Bu considering the similarity of spectral reflectance among three species of mangrove and other plants, we followed the concept of supervised classification to select a few training areas with known mangrove trees, where the training areas are determined from the detailed map of mangrove distribution derived from the field investigation. The Hourglass hyperspectral analysis technique was employed to identify the endmembers of pure mangrove in the training areas. The results are consistent with the current distribution of mangrove trees, and the remarkable feature of a “mangrove desert” highlights a fact that biodiversity can be easily and quickly destroyed if no protection is provided. Some remnant patches located by this research are very important to the management of mangrove trees.

Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images. We propose an unsupervised learning technique called spectral-spatial diffusion learning (DLSS) that combines a geometric estimation of class modes with a diffusion-inspired labeling that incorporates both spectral and spatial information. The mode estimation incorporates the geometry of the hyperspectral data by using diffusion distance to promote learning a unique mode from each class. These class modes are then used to label all the points by a joint spectral-spatial nonlinear diffusion process. A related variation of DLSS is also discussed, which enables active learning by requesting labels for a very small number of well-chosen pixels, dramatically boosting overall clustering results. Extensive experimental analysis demonstrates the efficacy of the proposed methods against benchmark and state-of-the-art hyperspectral analysis techniques on a variety of real data sets, their robustness to choices of parameters, and their low computational complexity.

Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation.

Understanding the spatial distribution of forage quality is important to address critical research questions in grassland science. Due to its efficiency and accuracy, there has been a widespread interest in mapping the canopy vegetation characteristics using remote sensing methods. In this study, foliar chlorophylls, carotenoids, and nutritional elements across multiple tallgrass prairie functional groups were quantified at the leaf level using hyperspectral analysis in the region of 470–800 nm, which was expected to be a precursor to further remote sensing of canopy vegetation quality. A method of spectral standardization was developed using a form of the normalized difference, which proved feasible to reduce the interference from background effects in the leaf reflectance measurements. Chlorophylls and carotenoids were retrieved through inverting the physical model PROSPECT 5. The foliar nutritional elements were modeled empirically. Partial least squares regression was used to build the linkages between the high-dimensional spectral predictor variables and the foliar biochemical contents. Results showed that the retrieval of leaf biochemistry through hyperspectral analysis can be accurate and robust across different tallgrass prairie functional groups. In addition, correlations were found between the leaf pigments and nutritional elements. Results provided insight into the use of pigment-related vegetation indices as the proxy of plant nutrition quality.

Sedimentary and drainage evolution of the Condamine Valley Transition Zone (eastern Australia)

The basal succession of the Condamine Valley, which overlays the boundary between the Surat and Clarence-Moreton basins (eastern Australia), contains a clay-rich horizon ‘the Transition Zone’ that marks a pronounced unconformity between the Jurassic Walloon Coal Measures and the Quaternary Condamine Alluvium. This paper provides insights into the tectonic and drainage evolution of the Condamine Valley through integrated analysis of U–Pb ages of detrital zircon from three samples (494 concordant analyses), stable oxygen isotope analysis on eight authigenic clay samples, X-ray fluorescence of primary and trace elements, and hyperspectral mineral analysis from two drill cores (Lone Pine 17 and Daandine 164). The Transition Zone is interpreted to consist of both weathered Jurassic sediments and Cenozoic clay deposits. Two sequential cycles of erosion, deposition and pedogenesis, related to geomorphological and climatic conditions are recognised. Distinctive oxygen isotope signatures of the two weathering fronts demonstrated an initial Early Cretaceous phase (δ18O = 11.9–15.7‰ VSMOW) associated with laterisation and possible uplift, followed by Paleogene (δ18O = 16.4–17.3‰ VSMOW) silcretisation of reworked Jurassic sediments. Detrital zircon geochronology yielded Jurassic maximum constraints for the age of deposition of three samples that are indistinguishable within error, the oldest of which (163 ± 8 Ma from the lateritic profile) corresponds to the age of the underlying Walloon Coal Measures. The two overlaying samples from a silcrete profile and granular alluvium yielded overlapping yet younger ages of 150 ± 6 Ma and 156 ± 9 Ma, respectively. Vitrinite reflectance used as a proxy for the thermal condition of the coal strata enabled an estimated 2–3 km of burial and subsequent (likely Early Cretaceous) uplift. Geochemical insights from the Condamine Valley correspond to broad-scale climatic and tectonic conditions, suggesting that ‘transition zone equivalents’ and corresponding groundwater dynamics may occur in similar sedimentary settings throughout eastern Australia.

Feasibility of hyperspectral analysis for discrimination of rabbit liver VX2 tumor.

BACKGROUNDHepatocellular carcinoma is one of the most common malignant tumors worldwide. Currently, the most accurate diagnosis imaging modality for hepatocellular carcinoma is enhanced magnetic resonance imaging. However, it is still difficult to distinguish cirrhosis lesions, and novel diagnosis modalities are still needed.AIMTo investigate the feasibility of hyperspectral analysis for discrimination of rabbit liver VX2 tumor.METHODSIn this study, a rabbit liver VX2 tumor model was established. After laparotomy, under direct view, VX2 tumor tissue and normal liver tissue were subjected to hyperspectral analysis.RESULTSThe spectral signature of the liver tumor was clearly distinguishable from that of the normal tissue, simply from the original spectral curves. Specifically, two absorption peaks at 600-900 nm wavelength in normal tissue disappeared but a new reflection peak appeared in the tumor. The average optical reflection at the whole waveband of 400-1800 nm in liver tumor was higher than that of the normal tissue.CONCLUSIONHyperspectral analysis can differentiate rabbit VX2 tumors. Further research will continue to perform hyperspectral imaging to obtain more information for differentiation of liver cancer from normal tissue.

Using diffuse reflectance spectroscopy for assessing soil phosphorus status of an intensively cropped region

Phosphorus (P) is an essential plant nutrient and plays a vital role in achieving potential yields in rice-wheat sequence. conventional analytical techniques for assessing plant available P in soil are time consuming, relatively expensive and are not environment friendly. Diffuse reflectance spectroscopy particularly visible-near infrared (VIS-NIR) reflectance spectroscopy is an effective alternative means for rapid, nondestructive and eco-friendly assessment of available phosphorus and other soil properties. Geo-referenced surface soil (0–15 cm) samples (170) were collected from a rice-wheat field in Ludhiana district of Punjab state for performing both chemical (using Olsen method) and hyper-spectral analysis (350–2500 nm). A multivariate modeling technique, namely partial least square regression (PLSR) was employed to develop spectral model. Statistical diagnostics of coefficient of determination (R2), root mean square of error of prediction (RMSEP) and ratio of performance deviation (RPD) were used to evaluate the predictive performance of the developed spectral model. the R2, RMseP and RPd values were 0.42, 1.15 and 1.40, respectively for separate validation dataset of the PLsR model. however, the R2 and RMseP were better when the same set was used as validation set by using one-leave-out method. the RPd value suggested acceptable prediction accuracy of the spectral model. however, low R2 value and somewhat higher RMseP suggested exploring new modelling or data preprocessing techniques to enhance prediction accuracy.

Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method

Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.

Collaborative Control Protocol for Agricultural Cyber-physical System

In this article, a Collaborative Control Protocol for Robotic and Cyber-Physical System (CCP-CPS) is presented. CCP-CPS, which enables an application of robotics in a CPS system for smart and precision agriculture (PA), aims to monitor and identify stresses in greenhouse crops by a hyperspectral analysis method. Roles of CCP-CPS are to assign tasks to agents, identify and resolve conflicts and errors in the system, and enable a more effective collaboration and interaction among agents in CPS, compared to traditional non-collaborative and non-CPS approach. Collaborative Control Theory (CCT) is utilized for two system levels; protocol and agent level (CCP level), and CPS and environment level (CPS level). We use two case studies to test and validate our method with alternative approaches. The results from computer simulation show that 1) CCP-CPS can identify the highest number of greenhouse locations containing stresses, 2) CCP-CPS can utilize available resources (time) effectively, and 3) CCP-CPS can respond to an emergency stress situation faster and have more tolerance capability with system conflicts and errors, by having human integration and cyber-augmented greenhouse system, than other monitoring systems.

Акустооптический гиперспектральный модуль для гистологического исследования микрообъектов

AbstractThe problem of the development of an adjustable video-spectrometer unit based on an acousto-optic tunable filter has been considered. This unit is embedded into an optical system of microscopes for hyperspectral analysis of microscopic objects. For this purpose, light–energy conjugation of the filter with microscopes is performed. We have calculated the conjugating optical system, which yields an equality of field of view for a wideband and spectral channel. The experimental model of the developed device has been described. Examples of detection for test objects and histological sections along with the calculation results of the spectral parameters for various skin tissues are given. The developed video-spectrometer unit considerably extends the functionality of microscopes and can be widely used to solve various problems of biomedical diagnostics.

Hyperspectral Data Analysis in R: The hsdar Package

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data.

Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: A case study of the Colorado River Ecosystem, Grand Canyon, Arizona

Riparian regions are essential habitats for wildlife and play a vital role in agricultural production, but they are highly dynamic environments impacted by fluctuations of water levels. Monitoring vegetation types along narrow river corridors is complicated and requires high-resolution imagery and advanced remote sensing techniques due to the mixture of vegetation and other types of land covers. The primary aim of this paper is to develop a framework using airborne imagery, object-based image approach (OBIA), hyper-spectral analysis and Random Forest to classify vegetation along narrow, semi-arid riparian corridors via a case study of the Grand Canyon, the Colorado River. By analyzing hyper-spectral and field data with Random Forest, we found that the bandwidths from 642 to 682 nm and 750 to 870 nm were useful for vegetation classification in this case study. As a result, the red and near-infrared bands of aerial photos were used with ancillary data for species classification, and the overall accuracy (OA) of classification with these images reached up to 94.8% with a Kappa's coefficient of 0.93. The similarity of vegetation phenology caused most of the misclassified cases. Low cost unmanned aerial vehicles should be used to acquire more frequent data, which is essential to understand how vegetation patterns change over time.

Improved algorithm for hyperspectral image classification

Due to the high-dimensional data space generated by hyperspectral sensors together with the real-time requirements of several remote sensing applications, it is important to accelerate hyperspectral data analysis. For this purpose, we aim to improve the performance of an existing classification algorithm and reduce its execution time. The proposed algorithm is based on sparse representation and using extended multiattribute profiles as spectral–spatial features, and sparse unmixing by variable splitting and augmented Lagrangian as the optimization method. The speeding up is mainly achieved by exploiting the interdependencies among iterative calls and providing an appropriate memorization technique to reduce the extra cost by factorizing the algebraic computations. The experimental results on two HSI data sets prove that the optimized algorithm is really faster than the original one while retaining the same classification accuracy. This study shows how useful it is to adapt the implementation of the generic module in order to become more appropriate to the application and to minimize the extra costs as much as possible.