Linear Spectral Unmixing Techniques Research Articles

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

<p>Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to addressing this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.<strong></strong></p>

In vivo spectroscopic photoacoustic imaging and laser-induced nanoparticle vaporization for anti-HER2 breast cancer.

This study reports on the development and application of theragnostic agents targeting the HER2 receptors in breast tumors. The agent was constructed by loading silica-coated gold nanorods (GNRs) and a perfluorohexane liquid into PLGA-PEG nanoparticles, followed by surface conjugation with antibody Herceptin. The particle uptake in human breast cancer MDA-MB-231 (HER2-negative) and BT474 (HER2-positive) cell lines was tested. A proof of principle in vivo study was also performed using a xenograft mouse bilateral tumor model (16 mice, 32 tumors). Photoacoustic imaging was performed using a VevoLAZR device at 720/750/850 nm illuminations and 21 MHz central frequency. The relative concentrations of GNRs in the tumor were quantified using a linear spectral unmixing technique. The therapeutic efficacy of these nanoparticles was evaluated through optical droplet vaporization, and cell damage was confirmed using tissue immunofluorescence and histology. Our results demonstrate the potential of PLGA-GNRs as theragnostic agents for anti-HER2 breast cancer therapy.

Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi

Spatial changes in urban areas are closely associated with the increasing impervious land and thus, monitoring the spatio-temporal changes in impervious area is crucial for identifying urban growth. The capital city of India, Delhi has become one of the most populated cities of the world for its fast-growing economy and infrastructural development. Although the city is expanding since the last couple of decades, the rate of growth has become significantly high in the previous decade. This study aims to identify the spatio-temporal pattern of impervious surface growth in and around Delhi National Capital Territory (NCT) by using bi-temporal Landsat images of 2003 and 2014. The linear spectral unmixing (LSU) technique was employed for assessing the impervious surface growth over the megacity. To understand the associated changes of such growth, vegetation surface fraction (VSF), land surface temperature (LST) and normalized difference vegetation index (NDVI) were estimated and compared with the impervious surface fraction (ISF). Further, the fractional abundance of impervious surface was validated with built-up density, urban expansion and population density of the area. This study reveals the significant growth of impervious land in the peri-urban centres surrounding Delhi. The fractional abundance of impervious surface was found highly correlated with the vegetation surface fraction, LST and NDVI. The significant (p < 0.005) correlation coefficients prove good agreement among these variables. Strong negative correlation (r2 = 0.857) between ISF and urban expansion index (UEI) proves the potentiality for urban expansion in the less developed areas with abundant pervious surface. The study also reveals a significant polynomial relationship (r2 = 0.746) between impervious surface fraction and population density indicating high ISF (0.9‐1.0) in the densely populated areas and vice-versa. The expanding impervious surface especially in the peri-urban centres along with the rising intensity of urban heat island (UHI) calling for suitable planning and strategies for sustainable urban growth.

Hyperspectral two-photon excitation microscopy using visible wavelength.

We demonstrate hyperspectral imaging by visible-wavelength two-photon excitation microscopy using line illumination and slit-confocal detection. A femtosecond pulsed laser light at 530 nm was used for the simultaneous excitation of fluorescent proteins with different emission wavelengths. The use of line illumination enabled efficient detection of hyperspectral images and achieved simultaneous detection of three fluorescence spectra in the observation of living HeLa cells with an exposure time of 1 ms per line, which is equivalent to about 2 µs per pixel in point scanning, with 160 data points per spectrum. On combining linear spectral unmixing techniques, localization of fluorescent probes in the cells was achieved. A theoretical investigation of the imaging property revealed high-depth discrimination property attained through the combination of nonlinear excitation and slit detection.

Estimating the fractional abundance of coral reef benthic compositions using linear spectral unmixing

The present work is focused on providing the application of linear spectral unmixing technique to Landsat-8 images of Operational Land Imager (OLI) bands in coral reef environment at Gulf of Mannar (GoM) islands along the coast of Tamilnadu, India. The spectral characteristics of benthic compositions namely sand, seagrass, branched corals and mass corals in the GoM are recorded in-situ using RAMSES-TriOS Hyperspectral radiometer and the signatures recorded from in-situ have been used as input for linear spectral unmixing technique. The results of linear spectral unmixing provide the fractional abundances of coral reef benthic communities in the Islands of GoM region. Such maps can be used to characterize the landscape structure and composition of reefs, which can in turn be linked to environmental and human impacts. This kind of approaches is cost-effective and acts as a valuable tool for the evaluation and periodical monitoring of coral reef community.

Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data

ABSTRACT Spaceborne data with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short Wave Infrared (SWIR) bands have been used worldwide in discriminating different clay minerals. However, due to wide bandwidth of ASTER sensors, it is difficult to distinguish between pure and impure variety of talc mineral. The present study highlights the efficacy of airborne hyperspectral data Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) for spectral analysis in talc mineral in the Jahazpur Belt of Bhilwara district in Rajasthan, India. Due to its better spectral and spatial resolution, AVIRIS-NG is found to be extremely useful in identifying talc mineral in SWIR region. Talc is characterized by doublet shaped spectral pattern in AVIRIS-NG image with strong absorption dips at 2289.75 nm and 2314.80 nm which could not be picked up in ASTER image. Spectral matching of talc pixels is verified with the field spectra of samples and available spectral library of United States Geological Survey, Jet Propulsion Laboratory, and International Geological Correlation Programme. Variation in reflectance percentage of different talc pixels is attributed to variation in chemical composition of talc. However, a large number of mixed pixels with spectral pattern of both talc and kaolinite minerals have been identified in AVIRIS-NG image which are found to be an impure variety of talc. Whole-pixel to sub-pixel classification techniques with Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, and Linear Spectral Unmixing (LSU) techniques have been applied for talc abundance mapping in Jahazpur area. LSU is found to be the most useful and accurate technique in classifying spectrally pure to impure variety of talc.

ASTER and WorldView-3 satellite data for mapping lithology and alteration minerals associated with Pb-Zn mineralization

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used for mapping lithological units and hydrothermal alteration zones associated with Pb-Zn mineralization in the Kerman–Kashmar Tectonic Zone (KKTZ), Iran. The visible near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) bands of ASTER were used to map iron oxide/hydroxides, Al-OH minerals, Fe,Mg-OH minerals, quartz and carbonate minerals. The VNIR bands of WV-3 were used to discriminate Fe3+ and Fe2+ absorption intensities. Selective Principal Component analysis (SPCA), Spectral Angle Mapper (SAM), Linear Spectral Unmixing (LSU) and Automatic Lineament Extraction techniques were implemented. Lithological units were discriminated based on Al/Fe-OH, Fe2+/Fe3+ and Mg-Fe-OH/CO3 absorption properties. The spatial distribution of hematite, goethite, jarosite, gypsum, calcite, dolomite, kaolinite and muscovite were comprehensively detected. Some prospective zones were identified in the intersection of N-S, NW-SE and NE-SW trending fault systems, gossan, argillic/phyllic and dolomitic units.

Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements

Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it’s Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.

Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data

High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature.

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

ABSTRACT Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data. A compact data representation without compromising the physical information and optimizing the separation between different materials are the main objectives of such selection processes. In this work, a hyperspectral band selection approach is proposed based on linear spectral unmixing and sequential clustering techniques. The use of these two specific techniques constitutes the main novelty of this investigation. The proposed approach operates in different successive steps. It starts with extracting material spectra contained in the considered data using an unmixing method. Then, the variance of extracted spectra samples is calculated at each wavelength, which results in a variances vector. This one is segmented into a fixed number of clusters using a sequential clustering strategy. Finally, only one spectral band is selected for each segment. This band corresponds to the wavelength at which a maximum variance value is obtained. Experiments on three real hyperspectral data demonstrate the superiority of the proposed approach in comparison with four methods from the literature.

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.

Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization

Hypersharpening aims at combining an observable low-spatial resolution hyperspectral image with a high-spatial resolution remote sensing image, in particular a multispectral one, to generate an unobservable image with the high spectral resolution of the former and the high spatial resolution of the latter. In this paper, two such new fusion methods are proposed. These methods, related to linear spectral unmixing techniques, and based on nonnegative matrix factorization (NMF), optimize a new joint criterion and extend the recently proposed joint NMF (JNMF) method. The first approach, called gradient-based joint-criterion NMF (Grd-JCNMF), is a gradient-based method. The second one, called multiplicative JCNMF (Mult-JCNMF), uses new designed multiplicative update rules. These two JCNMF approaches are applied to synthetic and semireal data, and their effectiveness, in spatial and spectral domains, is evaluated with commonly used performance criteria. Experimental results show that the proposed JCNMF methods yield sharpened hyperspectral data with good spectral and spatial fidelities. The obtained results are compared with the performance of two NMF-based methods and one approach based on a sparse representation. These results show that the proposed methods significantly outperform the well-known coupled NMF sharpening method for most performance figures. Also, the proposed Mult-JCNMF method provides the results that are similar to those obtained by JNMF, with a lower computational cost. Compared with the tested sparse-representation-based approach, the proposed methods give better results. Moreover, the proposed Grd-JCNMF method considerably surpasses all other tested methods.

Monitoring olive mills waste disposal areas in Crete using very high resolution satellite data

This paper evaluates the efficiency of different image analysis techniques applied to high resolution multispectral satellite data so as to identify olive oil waste disposal areas in the island of Crete where huge quantities of wastes are produced. For this purpose very high spatial resolution images including Pleiades, SPOT 6, QuickBird, WorldView-2 and GeoEye 1 have been exploited. The research included the application of the Normalised Difference Vegetation Index, Olive Oil Mill Waste Index as well as Principal Component Analysis. Moreover Intensity–Hue–Saturation transformation was carried out. Furthermore, unsupervised classification was performed for a variety of classes (5; 10 and 15) over the same area for two different periods. In addition, supervised linear constrained spectral un-mixing technique has been applied for the WorldView-2 image, to evaluate the potential use of sub-pixel analysis. Indeed, as it is demonstrated NDVI and OOMW indices may be used to enhance the exposure of disposal areas in high resolution satellite datasets, while the application of the PCA and HIS transformations seems to be able to further improve the results. Unsupervised classification techniques, with no ground truth data, can sufficiently work; however temporal changes of the disposal areas can affect the performance of the classifier. The use of spectral library was able to detect OOMW areas with a relatively high rate of success improving the results from the unsupervised classification. Finally, a COSMO-SkyMed radar image has been examined and fused with a hyperspectral EO-ALI image, indicating that such kind of datasets might be also explored for this purpose.

Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization

ABSTRACTPansharpening aims at combining observable panchromatic and multispectral images to generate an unobservable image with the high spatial resolution of the former and the spectral diversity of the latter. In this paper a new fusion method is proposed. This method, related to linear spectral unmixing (LSU) techniques and based on non-negative matrix factorization (NMF), optimizes, by new iterative–multiplicative update rules, a joint criterion that exploits a spatial degradation model between the two images. The proposed Multiplicative Joint Non-negative Matrix Factorization (MJNMF) approach is applied to synthetic and real data, and its effectiveness in spatial and spectral domains is evaluated with commonly used performance criteria. Experimental results show that the proposed method yields good spectral and spatial fidelities of the pansharpened data. Also, it outperforms those tested from the literature.

Growth of Dehradun city: An application of linear spectral unmixing (LSU) technique using multi-temporal landsat satellite data sets

In order to quantify and monitor the spatio-temporal growth of urban areas, remote sensing data and GIS techniques are getting attention for its unparallel utility by the city planners as well as decision makers. The present study aims to analyse the rapid increase of built-up area in Dehradun city, the capital of Uttarakhand, India. The study uses multi-temporal Landsat images to monitor the pattern of urban growth of Dehradun. To classify coarse satellite data the traditional supervised and unsupervised techniques are not very useful because of mixed pixels in the classified data and poor accuracy. In this situation linear spectral unmixing (LSU) is a very useful classification technique because it classifies land use land cover classes by estimating the fractional values of each class. The study shows that built-up area of the city has grown about 20 per cent during 2000–2010 which is also identical with the population growth of Dehradun when compared with the Census of India data. The accuracy of Landsat based LSU model was assessed using high resolution LISS IV data and the RMSE was 0.07 and 0.11 respectively for vegetation and built-up fraction classes. It is seen that built-up area in the Kaulagarh, Kargi, Deep Nagar, Indirapuram and Kawli wards of Dehradun city have experienced more than 30% increase mainly in the South and South-West part of the city. The multi-temporal, multi-scale maps of the Dehradun city generated from remote satellite data will be very useful for planning both at micro- and macro-levels and further to identify the potential areas for urban expansion and future planning keeping in view protecting the urban green landscape of the Himalayan city of Dehradun.

Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science

Desertification is considered as a major worldwide environmental problem mainly caused by the climate changes and human activities during the last decades. Areas affected by desertification processes are gradually losing their level of biological quality and productivity. Among the different indicators of desertification, degradation of vegetation cover and increasing amount of bare soil have been popularly used by researchers. In India, desertification is one of the major sluggish hazards which is found in northwestern part of this country, mainly in the state of Rajasthan. The infringement of the Thar Desert has become a serious problem in the adjoining districts of Bikaner, Churu, and Nagaur. In this study, a linear spectral unmixing (LSU) method has been used for end-member fraction estimation primarily to differentiate the sand percentage and vegetation cover percentage. This linear spectral unmixing model is a widely used technique in remote sensing to estimate the fractions of several individual surface components present in an image pixel and the pure reflectance spectrum of a component which is called end-member. The LSU technique is able to monitor desertification process in terms of fractional changes in bare soil (sand) and vegetation covers. These two land features are the most crucial indicator of desertification and their long-term changes can produce expected result in identification of desertification process in an area. The long-term multispectral satellite data such as Landsat Thematic Mapper (TM) (1990, 1995, and 1999) and Enhanced Thematic Mapper (ETM+) (2003, 2009) have been used in this study. The time series analysis of fractional images of vegetation cover and bare soil has been employed for monitoring desertification processes over a long period. After analyzing the changes, some distinct patches of vegetation depletion coupled with increasing bare soil fraction were identified within the region that clearly indicates the ongoing process of desertification over there.

Identification and mapping of some soil types using field spectrometry and spectral mixture analyses: a case study of North Sinai, Egypt

This study examines linear spectral unmixing technique for mapping the surface soil types using field spectroscopy data as the reference spectra. The investigated area is located in North Sinai, Egypt. The study employed data from the Landsat 7 ETM+ satellite sensor with improved spatial and spectral resolution. Mixed remotely sensed image pixels may lead to inaccurate classification results in most conventional image classification algorithms. Spectral unmixing may solve this problem by resolving those into separate components. Four soil type end-members were identified with minimum noise fraction and pixel purity index analyses. The identified soil types are calcareous soils, dry sabkhas, wet sabkhas, and sand dunes. Soil end-member reference spectra were collected in the field using an ASD FieldSpec Pro spectrometer. Constrained sum-to-one and non-negativity linear spectral unmixing model was applied and the soil types map was produced. The results showed that linear spectral unmixing model can be a useful tool for mapping soil types from ETM+ images.

Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil

Remote sensing techniques are being increasingly applied to investigate soil characteristics. Here we propose an approach that allows the detection of salt-affected soils in arid and semi-arid environments. We test the procedure in Northeast Brazil through a combination of remote sensing and geochemical ground-based measurements. Spectral indices were used to characterize soil salinization features and patterns. The Linear Spectral Unmixing technique (LSU) is applied in this study to improve the prediction of soil salinity. Eighteen indices were extracted from the MODIS Terra data. A moderate correlation was found between electrical conductivity and the spectral indices. An improvement occurs in most of the correlations after applying the LSU method. To generate a predicted salinity map, a multiple linear regression, based on the best correlated indices is conducted. The standard error of the estimate is about 12.1 μS cm −1.

Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

A staged approach for the application of linear spectral unmixing techniques to airborne hyperspectral remote sensing data of reef communities of the Al Wajh Barrier, Red Sea, is presented. Quantification of the percentage composition of four different reef components (live coral, dead coral, macroalgae and carbonate sand) contained within the ground sampling distance associated with an individual pixel is demonstrated. In the first stage, multiple discriminant function analysis is applied to spectra collected in situ to define an optimal subset combination of derivative and raw image wavebands for discriminating reef benthos. In the second phase, unmixing is applied to a similarly reduced subset of pre-processed image data to accurately determine the relative abundance of the reef benthos (R 2 > 0.7 for all four components). The result of a phased approach is an increased signal-to-noise ratio for solution of the linear functions and reduction of processing burdens associated with image unmixing.

Applying linear spectral unmixing to airborne hyperspectral imagery for mapping yield variability in grain sorghum and cotton fields

This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair of crop plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover abundance fractions. For comparison, the simulated broad-band normalized difference vegetation index (NDVI) and narrow-band NDVI-type indices involving all possible two-band combinations of the 102 bands in the hyperspectral imagery were calculated and related to yield. Statistical results showed that plant abundance fractions provided better correlations with yield than the broad-band NDVI and the majority of the narrow-band NDVIs, indicating that plant abundance maps derived from hyperspectral imagery can be used as relative yield maps to characterize yield variability in grain sorghum field and cotton fields without the need to choose the best NDVI. Moreover, the unconstrained plant abundance provided essentially the same results for yield estimation as the constrained plant abundance either with the abundance sum-to-one constraint only or with both the sum-to-one and non-negativity constraints, indicating that the more computationally complex constrained linear unmixing does not offer any advantage over the simple unconstrained linear unmixing for this application.