Sequential Maximum Angle Convex Cone Research Articles

Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods

Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.

Evaluating automated endmember extraction for classifying hyperspectral data and deriving spectral parameters for monitoring forest vegetation health.

The hyperspectral remote sensing datasets possess high capability in differentiating the spectrally similar features, and thus, they are immensely important in various forestry activities, especially vegetation classifications. But extracting endmembers for data training is a challenging task. The present study is focused on the use of automated endmember extraction technique for deriving endmembers during the unavailability of ground spectra. We used the Sequential Maximum Angle Convex Cone (SMACC) method on EO-1 Hyperion data for endmember extraction in the Barkot forest range of Dehradun district, Uttarakhand which were used for classification of the study area using support vector machine (SVM). Further, we estimated the vegetation health of the region by assigning the threshold weights for various derived environmental variables such as NDVI (Normalised Difference Vegetation Index), CRI (Carotenoid Reflectance Index), Anthocyanin Reflectance Index (ARI), Modified Simple Ratio (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI) and WBI (Water Band Index). Then, to further validate the health of the forest types, we correlated it with the Land Surface Temperature (LST) from LANDSAT 5 ETM + data. The results showed a high classification accuracy of 89.13%. The healthy vegetation area coverage of the area was about 78.6% with most healthy class as Tectona grandis and Shorea robusta and its correlation with LST showed lower temperature range in healthy vegetation areas and vice versa. The study was useful in determining the superiority of SMACC automated endmember extraction and estimating the vegetation health.

Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers

Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of >85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management.

Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran

This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area. The mineral endmembers and their abundances were therefore determined using a series of hyperspectral processing algorithms. The virtual dimensionality methods including principal component analysis (PCA), minimum noise fraction (MNF), singular valued decomposition (SVD), Harsanyi-Farrand-Chang (HFC)/ (NWHFC), and Hyperspectral signal subspace identification by minimum error (HySime) were applied to estimate the number of endmembers. Five pure pixel-based methods including pixel purity index (PPI), sequential maximum angle convex cone (SMACC), simplex growing algorithm (SGA), N-FINDR, and vertex component analysis (VCA) were then applied for extracting the spectra of endmembers. Clay, serpentine, mica, and zeolite group minerals were identified which are consistent with the geological investigations in the region. The detected minerals were then mapped by the fully constrained least square (FCLS) method. The functionality of the methods and their performances on HyMap and Hyperion data were surveyed by several criteria including the number of recognized endmembers, the matching score of extracted endmembers with the reference spectrum, the agreement of the estimated abundances maps with the relevant lithological units on the geological map, and the average reconstruction error (ARE). Two hybrid maps were generated by combining individual methods that were found highly consistent with the geological map. The XRD analysis of three chips rock samples of two indicative lithological units was used to additionally check the efficiency of the applied methods.

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble’s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos.

Sub-pixel Mapping of Copper- and Iron-Bearing Metamorphic Rocks Using ASTER Data: A Case Study of Toutak and Surian Complexes, NE Shiraz, Iran

Metamorphic rock units of Toutak and Surian complexes located in the Sanandaj–Sirjan metamorphic zone, NE Shiraz, Iran, were mapped using the matched filtering (MF) method on the visible near-infrared (VNIR) and shortwave infrared (SWIR) bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor. These complexes host magnetite and Cu–Zn–Ag volcanogenic massive sulfide (VMS) deposits. Mixed pixels of lithologic–mineralogic indicators of these deposits were detected to identify the prospecting areas. The spectra of end-members were extracted from imagery using sequential maximum angle convex cone (SMACC) model and were compared to the field and laboratory spectra to examine their characteristics. The key minerals including chlorite + epidote (greenschist), muscovite + biotite (mica schist), calcite + dolomite (marble), and muscovite + oligoclase (gneiss) were identified and were input to the algorithm for estimating the sub-pixel fractions of desired minerals. A generalized map of lithologies was then produced by combining the fraction maps of greenschist, mica schist, marble, and gneiss using a GIS. Finally, this lithologic association was applied to define the prospecting areas. The accuracy of results was assessed using R2 coefficient, field observations, and XRD analysis. The R2 coefficient was 0.74 for greenschist, 0.63 for mica schist, 0.65 for marble, and 0.53 for gneiss. The results also showed good agreement with field evidence, and thus, they can be used for finding potential targets for copper and iron deposits.

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.

Selenium-enriched soil mapping using airborne SASI images

Selenium (Se)isanimportanttraceelementthat is essential tohumanbeings. In the past, the Se concentration has mostly been obtained by field sampling and analysed under laboratory conditions. Unfortunately, this process is expensive, and the number of available samples is usually relatively small.A soil geochemical survey was conducted in conjunction with an airborne survey via hyperspectral remote sensing in the Chuangye Farm area, China. Twenty-five elements/oxides including Se were analysed in the samples, and the results showed that Se has a highly negative correlation with K. Using hyperspectral Shortwave Infrared Airborne Spectrographic Imager (SASI) data, the abundances of clay minerals were obtained through the sequential maximum angle convex cone (SMACC) classification operation. According to the abundances of clay minerals, the reflectance of clay minerals was obtained using the spectral retrieval method. Due to the correlation among K, Se, clay minerals and their spectral characteristics, a stepwise regression model was established using the results from the geochemical survey data and retrieved hyperspectral SASI data; then, the K and Se concentrations were predicted. The results of this study show that predicting the Se content in soil by using SASI images through the spectral retrieval of clay minerals in soil in conjunction with actual geochemical analysis results boasts a higher prediction accuracy than the use of the raw SASI images, and this prediction approach has been proven to be feasible.

Decomposition of mixed pixels in MODIS data using Bernstein basis functions

The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods.

VALIDATION OF EXTRACTED ENDMEMBERS FROM HYPERSPECTRAL IMAGES

Abstract. An essential step in the characterization of surface materials using hyperspectral image analysis is image classification using endmembers. Spectral unmixing is the best method for hyperspectral image classification. This method assumes that the pixel-topixel variability in scene results from varying proportions of spectral endmembers. Spectral endmembers can be derived from the imagery or measurements in the laboratory or field. The primary objective of this paper was to assess the ability of extracted endmembers against some different solutions for extraction of endmembers for hyperspectral image classification. In this paper, we compared the Pixel Purity Index (PPI) and the Sequential Maximum Angle Convex Cone (SMACC) as two popular methods of endmember extraction with library and field spectral. We used spectral information divergence for detection desirable endmembers from field spectral. For accuracy assessment of spectral mixture analysis and production of endmember abundance images for each of methods, the linear spectral unmixing algorithm is used. After a comparison between the results of these methods, it has been verified that field spectral have a better classification result in comparing with other endmember extraction methods. Also, the PPI has reliable results as an automatic endmember extraction method in comparing.

UNSUPERVISED ZONING OF CULTIVATION AREAS WITH SIMILAR CULTIVATION PATTERN IN GOLESTAN PROVINCE BASED ON THE VEGETATION PRODUCTS OF MODIS SENSOR

Abstract. The estimation of cultivation area and categorizing the agricultural product types is one of the prerequisites for achieving sustainable development in the agricultural studies. In this study, an unsupervised zoning the cultivation areas with the same cultivation pattern in Golestan province is on the agenda. Therefore, due to wide spatial range, high temporal resolution and easy access of 16-day products of the vegetation of the MODIS sensor which acquired in a year (From November 2017 to October 2018), these images are used in this research. In the proposed method, after the generating of NDVI vegetation time series as a hyper-cube and separating farmlands’ boundaries in Golestan province using the land-use map; the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm and the maximum number of product variation using the statistical information of the region (Obtained from the statistics centre of Iran) are used to extract endmembers of the hyper-cube. In the following, the timing responses of the NDVI, identified as endmembers, will be refined in the second phase. In this process, identifying and eliminating noise signals (unrelated to cultivating patterns) and integrating the same cultivating patterns will be on the agenda. At the last stage of the proposed method and after refinement of the endmembers, the hyper-cube is clustered by Spectral Angle Mapper (SAM) algorithm and the mapping of regions with the same cultivation pattern is produced. In the proposed method, the zoning of agricultural land is based solely on the statistical knowledge of the variety of cultivation and the results have led to the production of interconnected spatial parts. This is consistent with the reality of the spatial occurrence of similar cultivating patterns in a geographic area. On the other hand, the visual comparison of results with large scale satellite images illustrates that there is a significant relationship between clustering results and ground truth in terms of cultivating pattern. Obviously, such products can be used as initial layers of information to produce the results of a supervised classification with the aim of applying the cultivation area of a variety of agricultural products.

The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.

Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images

Observing and identifying blood cells is a direct way for early diagnosis of blood diseases. Traditional blood cell recognition methods are usually time-consuming and laborious tasks for medical staff. This paper proposed an efficient leukocyte recognition method based on microscopic hyperspectral imaging technology. In order to achieve better segmentation performance and further improve the representativeness of features, the sequential maximum angle convex cone algorithm and iterative self-organizing data analysis technique algorithm are combined to segment the leukocytes from microscopic hyperspectral images. In addition, the uniform and rotation invariant local binary pattern is adopted as a textural measurement of the leukocytes. Combined the texture features with shape and spectral features, support vector machine is used to classify the leukocytes into different types. Experimental results show that the proposed method provides higher segmentation and recognition accuracy compared with the existing method. Moreover, the addition of spectral features improves the recognition performance shows the potential diagnosis capacity of microscopic hyperspectral imaging technology.

Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data

Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting subpixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water-Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier’s performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs.

Using geochemical data for prospecting target areas by the sequential maximum angle convex cone method in the Manzhouli area, China

Using geochemical data for prospecting target areas by the sequential maximum angle convex cone method in the Manzhouli area, China

Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging, EO-1 Hyperion sensor

Applied in Djebel Meni (Northwestern of Algeria), this research highlights the results obtained from the supervised classification using the Spectral Angle Mapper (SAM) algorithm, through introducing the spectral signatures of illite, kaolinite, and montmorillonite, via Jet Propulsion Laboratory (JPL) spectral library. These results were compared to the ones of the SAM classification, which use spectral signatures obtained by the Sequential Maximum Angle Convex Cone (SMACC) endmembers extraction algorithm. This implies the ability to detect and identify any object present on the Earth’s surface, whether its nature is mineral, vegetal, or human made, from hyperspectral imaging. By extracting the spectral signatures with the SMACC algorithm and matching them to the current signatures of JPL spectral library, comparing spectral signatures with another is not an easy task. Indeed, for a better comparison and a more appropriate interpretation in the use of the SAM classification, the results obtained were very relatively convincing because, regarding very strong similarities. It appears also that the signatures extracted with SMACC occupy the same areas as those of the JPL spectral library. This method of detection and identification of any present object on the Earth’s surface is rather conclusive.

Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA)

This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What’s more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions.

Pioneer vegetation as an indicator of the geochemical parameters in abandoned mine sites using hyperspectral airborne data

A general problem that is associated with open-pit mining is the total displacement of the former landscape’s geocomponents. Overburden conveyor bridges have an irreversible impact on the environment. The consequence of this is a completely new system—the so-called “post-mining landscapes”. These are characterized by extreme geochemical milieus, especially acidity, which is caused by weathering processes (acid mine drainage). These extreme environments are often left to natural succession because of the enormous costs and poor prospects of a successful reclamation. This study investigated the relationship between pioneer vegetation (e.g., Calamagrostis epigejos) and the geochemical properties of heap material using hyperspectral airborne HyMap data in the west Bohemian lignite district near Sokolov (Czech Republic). Thus, a fully constrained linear spectral unmixing algorithm was applied to link the geochemical parameters with fractional abundances. Due to the limitation of pure surfaces in the data set of such fragmented and mixed areas, a three-stage semiautomated endmember extraction was developed. The first stage included the selection of pure surface types using a decision tree with a self-developed narrow band and normalized difference indices. The second stage extracted the potential endmembers using the sequential maximum angle convex cone algorithm. The third stage identified the final endmembers and eliminated the spectral outliers by comparing the extractions to field/laboratory spectral libraries. The abundance fraction image of the pioneer vegetation, which was generated by the linear spectral unmixing, showed the spatial distribution and density of the vegetation. A linear regression model was used to calibrate the abundance image against the pH value. The regression model successfully predicted the pH values of the soil (R 2 = 0.67, RPD = 1.42). This method demonstrated that hyperspectral remote sensing data could be used for ecological medium- and long-term monitoring, as well as for spatial assessments in post-mining areas. In addition, this approach works with a small amount of geochemical measurements and the sparse availability of endmembers.

Scattering based hyperspectral imaging of plasmonic nanoplate clusters towards biomedical applications.

A new optical scattering contrast-agent based on polymer-nanoparticle encapsulated silver nanoplates (PESNs) is presented. Silver nanoplates were chosen due to the flexibility of tuning their plasmon frequencies. The polymer coating preserves their physical and optical properties and confers other advantages such as controlled contrast agent delivery. Finite difference time domain (FDTD) simulations model the interaction of light with the nanoplates in different orientations in the cluster. Hyperspectral dark field microscopy (HYDFM) observes the scattering spectra of the PESNs. An unsupervised sequential maximum angle convex cone (SMACC) image analysis resolves spectral endmembers corresponding to different stacking orientations of the nanoplates. The orientation-dependent endmembers qualitatively agree with the FDTD results. For contrast enhancement, the uptake and spatial distribution of PESNs are demonstrated by an HYDFM study of single melanoma cells to result in an enhanced contrast of up to 400%. A supervised spatial mapping of the endmembers obtained by the unsupervised SMACC algorithm reveals spatial distributions of PESNs with various clustering orientations of encapsulated nanoplates. Our study demonstrates tunability in plasmonics properties in clustered metal nanoparticles and its utility for the development of scatter-based imaging contrast agents for a broad range of applications, including studies of single cells and other biomedical systems.