Linear Spectral Unmixing 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>

A phenology-based linear spectral unmixing method for rice varieties identification from Landsat 8 Satellite Image in Ngawi District, East Java, Indonesia

Abstract. Cipta IM, Jaelani LM, Sanjaya H. 2024. A phenology-based linear spectral unmixing method for rice varieties identification from Landsat 8 Satellite Image in Ngawi District, East Java, Indonesia. Biodiversitas 25: 1691-1702. Ngawi District is a prominent rice-producing district in East Java, Indonesia. Based on the BPS data from 2021, Ngawi District yielded a total of 786.49 thousand tons of paddy. Multiple strategies to enhance rice production include enhancing rice productivity, increasing paddy fields, and using effective land management techniques. Utilizing a superior breed of seeds can augment productivity. Remote sensing can be utilized to obtain such data, one of which is the Linear Spectral Unmixing (LSU) technique. The LSU approach is used to determine the proportion of a pure object's presence in a pixel. The Inpari 32 rice variety is the predominant type identified from the analysis of the processing outcomes utilizing satellite imagery. In addition, validation was performed by utilizing many sample locations distributed around Ploso Lor and Rejuno Villages, resulting in consistent findings between the data analysis and the actual conditions observed in the field. The RMSE calculation was conducted on both sets of processing results, and the most optimal RMSE value was achieved when processing was performed using Landsat 8 image data, yielding an RMSE value of roughly ±1.2%. The accuracy of the model was evaluated using a confusion matrix, which yielded an overall accuracy rate of 86.67%.

Modeling remote-sensing and geophysical data to delineate the favorable mineralization localities at Semna area, Central Eastern Desert, Egypt

Semna area is located within the Golden Triangle district in the Central Eastern Desert of Egypt. The study maintains using multisource datasets of remote sensing, aero-spectrometry, aero-magnetic, and field investigations for unraveling the ambiguities associated with the alteration zones at the targeted area. Three remote sensing mapping techniques including; constrained energy minimization, linear spectral unmixing, and mineral indices, were adopted to delineate the alteration zones. The γ-ray spectrometry data of K (%), K/eTh, K/eU, and F-parameter enabled mapping the potassium-enriched localities as indication on possible potassic alteration zones. The surface and subsurface linear structural features were delineated from the digital elevation model and aero-magnetic data, respectively. The outcomes of all the implemented datasets were integrated using GIS overlay modeling, producing an integrated mineralization favorability/potentiality map. Eighteen localities with high potential for mineralization were highlighted. A field study was conducted to the investigated area during which, several alteration indicators were observed, including quartz veins, iron oxides staining, kaolinite, malachite, carbonate, and muscovite (sericite) alteration indicator minerals.

Optimizing clinical O2 saturation mapping using hyperspectral imaging and diffuse reflectance spectroscopy in the context of epinephrine injection.

Clinical determination of oxygen saturation (sO2) in patients is commonly performed via non-invasive optical techniques. However, reliance on a few wavelengths and some form of pre-determined calibration introduces limits to how these methods can be used. One example involves the assessment of sO2 after injection of local anesthetic using epinephrine, where some controversy exists around the time it takes for the epinephrine to have an effect. This is likely caused by a change in the tissue environment not accounted for by standard calibrated instruments and conventional analysis techniques. The present study aims to account for this changing environment by acquiring absorption spectra using hyperspectral imaging (HSI) and diffuse reflectance spectroscopy (DRS) before, during, and after the injection of local anesthesia containing epinephrine in human volunteers. We demonstrate the need to account for multiple absorbing species when applying linear spectral unmixing in order to obtain more clinically relevant sO2 values. In particular, we demonstrate how the inclusion of water absorption greatly affects the rate at which sO2 seemingly drops, which in turn sheds light on the current debate regarding the time required for local anesthesia with epinephrine to have an effect. In general, this work provides important insight into how spectral analysis methods need to be adapted to specific clinical scenarios to more accurately assess sO2.

Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery

Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

ABSTRACT Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.

Remotely characterizing photosynthetic biocrust in snowpack-fed microhabitats of Taylor Valley, Antarctica

Microbial communities are the primary drivers of carbon cycling in the McMurdo Dry Valleys of Antarctica. Dense microbial mats, consisting mainly of photosynthetic cyanobacteria, occupy aquatic areas associated with streams and lakes. Other microbial communities also occur at lower densities as patchy surface biological soil crusts (hereafter, biocrusts) across the terrestrial landscape. Multispectral satellite data have been used to model microbial mat abundance in high-density areas like stream and lake margins, but no previous studies have investigated the lower detection limits of biocrusts. Here, we describe remote sensing and field-based survey and sampling approaches to study the detectability and distribution of biocrusts in the McMurdo Dry Valleys. Using a combination of multi- and hyperspectral tools and spectral linear unmixing, we modeled the abundances of biocrust in eastern Taylor Valley. Our spectral approaches can detect low masses of biocrust material in laboratory microcosms down to biocrust concentrations of 1% by mass. These techniques also distinguish the spectra of biocrust from both surface rock and mineral signatures from orbit. We found that biocrusts are present throughout the soils of eastern Taylor Valley and are associated with diverse underlying soil communities. The densest biocrust communities identified in this study had total organic carbon 5x greater than the content of typical arid soils. The most productive biocrusts were located downslope of melting snowpacks in unique soil ecosystems that are distinct from the surrounding arid landscape. There are similarities between the snowpack and stream sediment communities (high diversity of soil invertebrates) as well as their ecosystem properties (e.g., persistence of liquid water, high transfer of available nutrients, lower salinity from flushing) compared to the typical arid terrestrial ecosystem of the dry valleys. Our approach extends the capability of orbital remote sensing of photosynthetic communities out of the aquatic margins and into the drier soils which comprise most of this landscape. This interdisciplinary work is critical for measuring and monitoring terrestrial carbon stocks and predicting future ecosystem dynamics in this currently water-limited but increasingly dynamic Antarctic landscape, which is particularly climate-sensitive and difficult to access.

Two-photon excitation fluorescence microspectroscopy protocols for examining fluorophores in fossil plants

Fluorescence emission is common in plants. While fluorescence microscopy has been widely used to study living plants, its application in quantifying the fluorescence of fossil plants has been limited. Fossil plant fluorescence, from original fluorophores or formed during fossilization, can offer valuable insights into fluorescence in ancient plants and fossilization processes. In this work, we utilize two-photon fluorescence microspectroscopy to spatially and spectrally resolve the fluorescence emitted by amber-embedded plants, leaf compressions, and silicified wood. The advanced micro-spectroscope utilized, with its pixel-level spectral resolution and line-scan excitation capabilities, allows us to collect comprehensive excitation and emission spectra with high sensitivity and minimal laser damage to the specimens. By applying linear spectral unmixing to the spectrally resolved fluorescence images, we can differentiate between (a) the matrix and (b) the materials that comprise the fossil. Our analysis suggests that the latter correspond to durable tissues such as lignin and cellulose. Additionally, we observe potential signals from chlorophyll derivatives/tannins, although minerals may have contributed to this. This research opens doors to exploring ancient ecosystems and understanding the ecological roles of fluorescence in plants throughout time. Furthermore, the protocols developed herein can also be applied to analyze non-plant fossils and biological specimens.

Effects of skin tone on photoacoustic imaging and oximetry.

Photoacoustic imaging (PAI) provides contrast based on the concentration of optical absorbers in tissue, enabling the assessment of functional physiological parameters such as blood oxygen saturation (). Recent evidence suggests that variation in melanin levels in the epidermis leads to measurement biases in optical technologies, which could potentially limit the application of these biomarkers in diverse populations. To examine the effects of skin melanin pigmentation on PAI and oximetry. We evaluated the effects of skin tone in PAI using a computational skin model, two-layer melanin-containing tissue-mimicking phantoms, and mice of a consistent genetic background with varying pigmentations. The computational skin model was validated by simulating the diffuse reflectance spectrum using the adding-doubling method, allowing us to assign our simulation parameters to approximate Fitzpatrick skin types. Monte Carlo simulations and acoustic simulations were run to obtain idealized photoacoustic images of our skin model. Photoacoustic images of the phantoms and mice were acquired using a commercial instrument. Reconstructed images were processed with linear spectral unmixing to estimate blood oxygenation. Linear unmixing results were compared with a learned unmixing approach based on gradient-boosted regression. Our computational skin model was consistent with representative literature for in vivo skin reflectance measurements. We observed consistent spectral coloring effects across all model systems, with an overestimation of and more image artifacts observed with increasing melanin concentration. The learned unmixing approach reduced the measurement bias, but predictions made at lower blood still suffered from a skin tone-dependent effect. PAI demonstrates measurement bias, including an overestimation of blood , in higher Fitzpatrick skin types. Future research should aim to characterize this effect in humans to ensure equitable application of the technology.

An Elastic-Window-Based Method for the Underdetermined Problem in Linear Spectral Unmixing to Enhance the Spatial Resolution of the Normalized Difference Vegetation Index Time Series

Inverting land cover reflectance or derived indices from low-spatial-resolution images to refine the spatial resolution of this data is cost-effective for land surface monitoring applications that face technical or budget limitations. Based on the linear spectral mixing model, many approaches have successfully unmixed coarse mixed pixels using high-spatial-resolution land cover maps in the past decades. However, in some cases, the solutions of linear systems composed of several mixed pixels may not be acquired due to the underdetermined problem. This study presents the causes of this problem and proposes an iterative approximation strategy to address it. An elastic-window-based algorithm was developed, where the initial size of the window was calculated based on the land cover of the mixed pixel. Mixed pixels of neighborhoods with similar land covers were then selected to form the unmixing linear system, which was examined through a simulation test to ensure it was not underdetermined. Otherwise, the window would expand to include more adjacent pixels. This process was repeated until a successful solution was obtained. A statistical analysis of sixty land cover maps from around the globe shows that the underdetermined problem exists at a low level but becomes more serious with an increase in mixed scale. The results demonstrate that the proposed algorithm effectively prevents the underdetermined problem for mixed pixels of different scales and can be integrated into the coarse NDVI downscaling procedure to refine spatial resolution. This study provides a reference for estimating underdetermined mixed pixels and benefits applications that require dealing with the inversion of land cover values directly.

Mapping and discrimination of the mineralization potential in granitoids from Banyo area (Adamawa, Cameroon), using Landsat 9 OLI, ASTER images and field observations

The Banyo area is situated in the west Cameroon domain of the Central African Fold Belt. In this study, field data and remote sensing conventional mapping techniques including Red-Green-Blue (RGB) colour combination, principal component analysis (PCA), Crosta-PCA, Band Ratios (BR), Constrained Energy Minimization (CEM), Minimum Noise Fraction (MNF), Spectral Angle Mapper (SAM) classification and Linear Spectral Unmixing (LSU) algorithms were used to process Landsat 9 OLI and ASTER data in order to discriminate various rock units such as biotite-amphibole granites, hornblende-biotite orthogneiss and map associated mineral deposits (e.g., iron-oxide, hydroxyl and corundum minerals). In addition, it was found that RGB combination (743) of OLI bands, PCA of OLI bands (PC123), BR of OLI bands (4/2, 6/7, 6/5), BR of ASTER (4/2, 7/6, 8/9), Crosta-PCA of OLI bands, MNF (123) of OLI bands, SAM, and LSU techniques applied to ASTER bands ascertain potential locations of granitoids and gneisses bearing iron and hydroxyl minerals. On the other hand, this study also revealed that corundum deposits are detected by applying the CEM method to OLI band ratio 6/7 highlighting red tones and processing SAM classification algorithm both using laboratory reference spectra. Automatic (using PCI Geomatica line tool) and manual extractions of lineaments revealed three major trends (N-S, E-W, and WNW-ESE to NW-SE) and an accessory trend (NE-SW to ENE-WSW) that surround hydrothermally altered rocks recognized as containing iron oxide, hydroxyl, and corundum mineral deposits. In the field, these lineaments may group to the tree deformation phases. The D1 phase (NW-SE); the D2 phase (NE-SW to ENE-WSW) and the D3 phase (N-S). Maximum Likelihood Calssification (MLC) and SAM classification methods showed to be more effective in discriminating rocks bearing Iron-oxide, hydroxyl and corundum minerals in the study area.This result permits to confirm that, the combination of remote sensing (Landsat 9 OLI and ASTER data in this case) and field data constitute a useful tool for mineral exploration in the shear zone. However, the accuracy assessment was relied on comparing the different maps produced from Landsat 9 OLI and ASTER data with the ground truth sample points and also, with the earliest geological map of Banyo area. Thus, quantitatively the results were accurate at 99%.

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Solar-induced chlorophyll fluorescence (SIF) is closely related to plant photosynthetic activity and has been used in different studies as a proxy for vegetation health status. However, in order to use SIF as a relevant indicator of plant physiological stress, it is necessary to accurately quantify the amount of light absorbed by the photosynthetic plant pigments, called the absorbed photosynthetically active radiation (APAR). The ratio between fluorescence emission and light absorption (i.e., SIF and APAR) is known as the fluorescence quantum efficiency (FQE). In this work, simultaneous measurements of SIF and reflected radiance were performed both at the leaf and canopy levels for Salvia farinacea and Datura stramonium plants. With the aim of disentangling the proportion of sunlit and shaded absorbed PAR, an ad hoc experimental setup was designed to provide a wide range of fraction vegetation cover (FVC) canopy settings. A linear spectral unmixing method was proposed to estimate the contribution of soil, sunlit, and shaded vegetation from the total reflectance spectrum measured at the canopy level. Later, the retrieved sunlit FVC (FVCsunlit) was used to estimate the (dominant) green APAR flux, and this was combined with the integral of the spectrally resolved fluorescence to calculate the FQE. The results of this study demonstrated that under no-stress conditions and independently of the FVC, similar FQE values were observed when SIF was properly normalised by the green APAR flux. The results obtained showed that the reflectance spectra retrieved using a linear unmixing method had a maximum RMSE of less than 0.03 along the spectrum. The FVCsunlit evaluation showed an RMSE of 14% with an R2 of 0.84. Moreover, the FQE values obtained at the top of the canopy (TOC) were found statistically comparable to the reference values at the leaf level. These results support further efforts to improve the interpretation of fluorescence based on field spectroscopy and the further upscaling to imaging spectroscopy at airborne and satellite levels.

Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery

The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year−1. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ).

Spectral mapping of zeolite bearing paleolake deposits at Lake Tecopa, California and its implications for mapping zeolites on Mars

• Zeolitic tuff at paleolake Tecopa investigated using in-situ and orbital methods. • Reasons for limited orbital detections of zeolites in paleolake. • Importance of ground investigation to interpreting orbital data. While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases. :

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. We developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (percentage of upper tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470–2819 mm year−1). Among these sites, we evaluated the PlanetScope-derived deciduousness against corresponding measures derived from WorldView-2 (n = 9 sites) and local phenocams (n = 9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90 m × 90 m) with r2 = 0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r2 ranging from 0.62 to 0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

Multiview Spatial–Spectral Two-Stream Network for Hyperspectral Image Unmixing

Linear spectral unmixing is an important technique in the analysis of mixed pixels in hyperspectral images. In recent years, deep learning-based methods have been garnering increasing attention in hyperspectral unmixing; especially, unsupervised autoencoder (AE) networks that have achieved excellent unmixing performance are a recent trend. While most approaches use spatial information, it is well known that hyperspectral data are characterized by a large number of narrow spectral bands. In order to take full advantage of the hyperspectral bands in unmixing and the spatial information, in this article, we explore multiview spectral and spatial information in an AE-based unmixing framework. We introduce multiview spectral information through spectral partitioning and propose a multiview spatial–spectral two-stream network, called MSSS-Net, which simultaneously learns a spatial stream network and a multiview spectral stream network in an end-to-end fashion for more efficient unmixing. The MSSS-Net is a two-stream deep unmixing network sharing a decoder, where its two AE networks employ recurrent neural networks (RNNs) to collaboratively utilize multiview spectral and spatial information. The spatial stream network branch extracts the spatial features of pixels and its neighbors, while the multiview spectral stream network branch exploits the multiview spectral bands of a pixel. Meanwhile, we design a cascaded bidirectional and unidirectional RNNs’ encoder structure for multiview spatial–spectral information to learn more discriminative deep patch-pixel features. Extensive ablation studies and experiments on both synthetic and real datasets demonstrate the superiority of the MSSS-Net over state-of-the-art unmixing methods.

Hyperspectral Image Mixed Noise Removal via Nonlinear Transform-Based Block-Term Tensor Decomposition

Recently, block-term decomposition with rank-( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L_r</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L_r</i> ,1) (termed as LL1 decomposition), which is physically inspired by linear spectral unmixing, has received increasing attention in hyperspectral images (HSIs) denoising. However, due to the intrinsic nonlinear structure of real-world HSIs, the low-rankness of HSIs is usually implicit. Moreover, the essential uniqueness guarantee is usually violated with the low-rank assumption of the abundance maps unsupported in real scenarios, which hampers the successful deployment of LL1 decomposition. Inspired by the nonlinear spectral unmixing, we propose a nonlinear learnable transform-based LL1 decomposition (NT-LL1) for characterizing the implicit low-rank structure of real-world HSIs. More concretely, the nonlinear learnable transform in NT-LL1 decomposition is a composed transform consisting of a linear semi-orthogonal transform and a component-wise nonlinear transform, which collaboratively enhances the low-rankness of the abundance maps. Empowering with the NT-LL1 decomposition, we propose an NT-LL1 decomposition-based model for HSIs denoising. To tackle the resulting model, we develop an efficient proximal alternating minimization-based algorithm with a convergence guarantee. Extensive experimental results including simulated and real data collectively verify the superiority of the proposed method as compared with the competing methods.

Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.

Mapping of ferric (Fe3+) and ferrous (Fe2+) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

ABSTRACT Iron plays an important role in industrial and engineering fields development of a country and as such there is an enormous demand for iron in Ethiopia. However, a search for this valuable primary mineral resource exploration remains challenging and costly. Therefore, this study aims to map iron oxide minerals using Landsat-8/operational land imager (OLI) and advanced space-borne thermal emission and reflection (ASTER) satellite imagery in Negash Lateritic iron deposit, Northern Ethiopia to ease the costs and reduce the time. Different image processing techniques such as band ratio, selective principal component analysis, linear spectral unmixing, and mixture-tuned matched filter were used to produce iron oxide maps. Minimum noise fraction (MNF), pixel purity index (PPI), and N-dimensional visualizer were also applied to extract endmembers in the automated spectral hourglass wizard. In addition to this, the enhanced image thresholding and scatter plot were used to map the potential areas. Ferric iron oxide band ratio of ASTER mapped maximum area of 62.1 km2 followed by a laterite band ratio of ASTER covering 57.8 km2. The result was validated using existing iron oxide polygons and the outcome obtained from selective PCA shows a strong match with the existing iron oxide polygons. The sub-pixel mapping techniques show poor accuracy in mapping goethite and hematite relative to the pixel level. Thus, it is evident from the results that ASTER mapped better than Landsat 8 OLI for band ratios of selective PCA, unmixing, MTMF, and mineralized areas while characterizing with limited fieldwork.

Estimating Time of Urbanization with Moderate-Resolution Sensors

The continuous recording of images of the Earth’s surface through Earth observation satellites has enabled the study of large-scale dynamical process, such as urbanization. Mapping the urban land cover at a given time is a first-order problem that has been largely addressed, and for which several acceptable solutions exist. A second-order problem is the analysis of change through time, which has been generally addressed through change vector or change matrix analyses, but they tend to neglect the spatial dimensions in favor of the temporal one. In this study, we propose the estimation of the time of urbanization across space as a useful indicator of the spatiotemporal structure of the urbanization process. A map of the time of urbanization can be considered a third-order product, as it compresses time series of urban land cover in one image that can effectively help identify areas of accelerated development and the time when they occurred. We apply linear spectral unmixing to quarterly composites of Landsat 8 imagery over the metropolitan corridor in central Mexico, from which a time series of urban percentage coverage is generated for the period between 2013 and 2021. Time filters were then applied to the time series and the time of urbanization was detected as the time when an increase of 25 percent of urbanization or larger first occurred. The accuracy assessment of the urbanization time showed a good correspondence with reference points that were randomly selected in the area of study.