Hyperspectral unmixing is a hot topic in signal and image processing. A set of high-dimensional data matrices can be decomposed into two sets of non-negative low-dimensional matrices by Non-negative matrix factorization (NMF). However, the algorithm has many local solutions because of the non-convexity of the objective function. Some algorithms solve this problem by adding auxiliary constraints, such as sparse. The sparse NMF has a good performance but the result is unstable and sensitive to noise. Using the structural information for the unmixing approaches can make the decomposition stable. Someone used a clustering based on Euclidean distance to guide the decomposition and obtain good performance. The Euclidean distance is just used to measure the straight line distance of two points. However, the ground objects usually obey certain statistical distribution. It׳s difficult to measure the difference between the statistical distributions comprehensively by Euclidean distance. Kullback–Leibler divergence (KL divergence) is a better metric. In this paper, we propose a new approach named KL divergence constrained NMF which measures the statistical distribution difference using KL divergence instead of the Euclidean distance. It can improve the accuracy of structured information by using the KL divergence in the algorithm. Experimental results based on synthetic and real hyperspectral data show the superiority of the proposed algorithm with respect to other state-of-the-art algorithms.

The detection of hydrous minerals on Mars is of great importance for revealing the early water environment as well as possible biotic activity. However, few studies focus on abundance retrieval of hydrous minerals for some difficulties. In this paper, we studied the area around the Mars Science Laboratory (MSL) landing site, to identify hydrous minerals and retrieve their abundance. Firstly, the distribution of hydrous minerals was extracted using their hydration features. Then, a sparse unmixing algorithm was applied along with the CRISM spectral library to retrieve the abundance of hydrous minerals in this area. As a result, seven hydrous minerals were retrieved, i.e. actinolite, montmorillonite, saponite, jarosite, halloysite, szomolnokite and magnesite and, the total concentration of all hydrous minerals was as high as 40聽vol% near the lower reaches of Mount Sharp. Our results were consistent with results from related research and the in-situ analysis of the MSL rover Curiosity.

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R~2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Tree species information is crucial for forest ecology and management, and development of techniques efficient for tree species classification has long been highlighted. In order to fulfil this task, a large variety of remote-sensing technologies have been attempted. Static terrestrial laser scanning (TLS) is such a representative case, which has proved to be capable of deriving explicit tree structure feature parameters (ETSPs) and has been primarily validated for tree species classification. However, in practice for each forest plot mapped by TLS, this kind of ETSP-based solutions can only work for the first circle layer of individual trees surrounding the TLS systems, because the trees at the outer circle layers tend to show incomplete crown representations due to the effect of laser obscuration. This adverse circumstance even may occur to the scenario of TLS-based inventory in the multi-scan mode. To break through this restriction, this study focused on tree stems that tend to be more readily mapped by TLS in the complicated forest environment, and then, their comparatively complete forms were used to comprehensively derive primarily stem-related feature parameters (SRPs) for distinguishing different tree species. Specifically, in this study 14 SRPs were proposed, mainly based on stem structure and surface texture characteristics. Based on a Support Vector Machine (SVM) classifier, the classification was operated in the leave-one-out cross-validation (LOOCV) mode. In the case of four typical boreal tree species, that is, Picea abies, Pinus sylvestris, Populus tremula, and Quercus robur, tests showed that the optimal total classification accuracy reached 71.93%. Given that tree stems generally display less features than crowns, the result is acceptable. Overall, the positive results have validated the strategy of fulfilling TLS-based tree species classification by deriving predominantly stem-related feature parameters, and this, in a broad sense, can expand the effective range of TLS on forest ecological studies.

Abstract We fabricated a visible-near-infrared (Vis-NIR) portable field imaging spectrometer with a prism-grating-prism element and a scanning mirror. The developed Vis-NIR imaging spectrometer, consisting of an INFINITY 3-1 detector and a V10E spectrometer from Specim Corporation, is designed to measure the spectral range between 0.4 and 1 碌m with spectral resolution of 2-4 nm. In recent years, sulfur fumigation has been abused during the processing of certain freshly harvested Chinese herbs. Fourier transform infrared spectroscopy, fiber optic NIR spectrometry, and liquid chromatography-mass spectrometry are typically used to analyze the chemical profiles of sulfur-fumigated and sun-dried Chinese herbs. Field imaging spectrometry is rarely used to identify sulfur-fumigated herbs. In this study, field imaging spectrometry, principal component analysis, and the partial least squares-discriminant analysis multivariate data analysis method are used to distinguish sun-dried and sulfur-fumigated Chinese medicinal herbs with a sensitivity of 96.4% and a specificity of 98.3% for RPA identification. These results suggest that hyperspectral imaging is a potential technique to control medicine quality for medical applications

The interfacial properties of three dimensional (3D) Nextel鈩440 fiber-reinforced silica (N440/SiO2) ceramic matrix composites containing a single layer pyrocarbon (PyC) interphase were investigated. The fiber/matrix bonding was characterized by the fiber push-in tests. The results showed that the PyC interphase can significantly weaken the interfacial bonding. The three-point bending tests indicated that the PyC interphase played a key role in the strength improvement of the composites. The fiber/matrix interface was observed by SEM and TEM in order to clarify the mechanism. Microstructure analysis revealed that the PyC interphase was chemically inert to the fiber and the matrix, and that the interfacial reaction was dramatically restrained. Moreover, the inherent layer structure of PyC proved to be very beneficial for crack deflection. Finally, the weakening occurred between the fiber and the coating, between the coating and the matrix, and within the coating.

Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetation’s biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coef- ficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related applications.

Harnessing solar energy through photovoltaic (PV) generation of electricity is a promising method, expected to reduce greenhouse gas emissions at a relatively low cost. A primary obstacle for the large-scale exploitation of solar energy in regions with poor electrical infrastructure is that the output of the PV systems is hard to match their connection with the electric grid, due to the lack of strategical planning. This study aims to map the most promising locations for potential PV investments in Tibet, China, where solar radiation is in abundance, presenting an opportunity to install PV stations across the country. Geographic information science (GIS) overlay was implemented, considering solar energy distribution, local terrain and native land cover. Several remotely sensed data were employed as input, including time series of solar radiation data, land cover data and digital elevation model data. In total, 4005 sites were selected, with the majority in the regions of Shigatse and Ngari. The results were discussed according to their distance to existing electricity substations, to evaluate the difficulty to be connected to the grid. The work highlights a method for the selection of suitable PV power generation sites, and provides a guidance for the construction of these stations, particularly in Tibet-like regions with poor electrical infrastructure, and harsh environmental conditions.

The significance of laser return intensity has been widely verified in airborne light detection and ranging (LiDAR)-based forest canopy mapping, but this does not mean that all of its roles have been played. People still ask such questions as "Is it possible using this optical attribute of lasers to investigate individual tree-crown insides wherein laser intensity data are typically yielded in complicated echo-triggering modes?" To answer this question, this study examined the characteristics of the intensities of the laser points within 10 Quercus robur trees by fitting their peak amplitudes into default Gaussian distributions and then analyzing the resulting asymmetric tails. Exploratory data analyses showed that the laser points lying within the distribution tails can indicate primary tree branches in a sketchy way. This suggests that the question can be positively answered, and the traditional restriction of airborne LiDAR in canopy mapping at the crown level has been broken. Overall, this study found a unique way to detect primary tree branches in airborne LiDAR data and pointed out how to explore more ways this optical intensity attribute of airborne LiDAR data can measure tree organs at fine scales and further learn their properties. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.

Hyperspectral remote sensing has been verified to be an effective tool in mineral information extraction. The existence of vegetation can cause a major problem for mineral exploration, and diagnostic absorption feature has the potential to be the key factor in mineral information extraction from a vegetation-covered area. This letter presents a new approach of mineral absorption feature extraction in high-density vegetation area based on reference spectral background removal. The resulting model can simulate the background curve based on the reference spectral background and eliminate the influence of vegetation through the background removal process. Experiments on simulated data validated its great potential in both qualitative and quantitative analyses for mineral exploration in vegetation-covered area.

We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought. We found significant positive relationships between SIF and drought indices during the growing season (from June to September). SIF was found to be more sensitive to short-term SPIs (one or two months) and less sensitive to long-term SPI (three months) than were the normalized difference vegetation index (NDVI) or the normalized difference water index (NDWI). Significant correlations were found between SIF and PDSI during the growing season for the Great Plains. We found good consistency between SIF and flux-estimated gross primary production (GPP) for the years studied, and synchronous declines of SIF and GPP in an extreme drought year (2012). We used SIF to monitor and assess the drought that occurred in the Great Plains during the summer of 2012, and found that although a meteorological drought was experienced throughout the Great Plains from June to September, the western area experienced more agricultural drought than the eastern area. Meanwhile, SIF declined more significantly than NDVI during the peak growing season. Yet for senescence, during which time the reduction of NDVI still went on, the reduction of SIF was eased. Our work provides an alternative to traditional reflectance-based vegetation or drought indices for monitoring and assessing agricultural drought.

Phyllosilicate belongs to hydrated silica, which is a principal form of hydrous minerals on the martian surface. It’s also an indicator in comparing different sediments and degree of aqueous alteration. Therefore, it’s essential to establish its recognition model for studying the geologic evolution of the Mars. Short-wave infrared (SWIR) spectral bands and thermal infrared (TIR) spectral bands have distinct spectral response to the mineral groups and ions, so they have distinctive advantages in detecting minerals. However the method of combining SWIR and TIR to recognize phyllosilicate is rarely studied. Based on the USGS spectral library, facing Compact Reconnaissance Imaging Spectrometer for Mars(CRISM) and Thermal Emission Imaging System(THEMIS),we conducted the research on the mechanism of the spectral response of phyllosilicate, and established the SWIR and TIR identification model respectively, then combined the SWIR and TIR spectral features to build the combined recognition model of phyllosilicate with Fisher discriminant analysis. The results of cross validation show that the identification accuracy of combined model is the highest, which can correctly classify 90.6% of the mineral samples and improve the identification precision of phyllosilicate effectively.

Locality-preserving projection (LPP) is a typical manifold-based dimensionality reduction (DR) method, which has been successfully applied to some pattern recognition tasks. However, LPP depends on an underlying adjacency graph, which has several problems when it is applied to hyperspectral image (HSI) processing. The adjacency graph is artificially created in advance, which may not be suitable for the following DR and classification. It is also difficult to determine an appropriate neighborhood size in graph construction. Additionally, only the information of local neighboring data points is considered in LPP, which is limited for improving classification accuracy. To address these problems, a modified version of the original LPP called MLPP is proposed for hyperspectral remote-sensing image classification. The idea is to select a different number of nearest neighbors for each data point adaptively and to focus on maximizing the distance between nonnearest neighboring points. This not only preserves the intrinsic geometric structure of the data but also increases the separability among ground objects with different spectral characteristics. Moreover, MLPP does not depend on any parameters or prior knowledge. Experiments on two real HSIs from different sensors demonstrate that MLPP is remarkably superior to other conventional DR methods in enhancing classification performance.

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS).We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

Numerous drought indices have been developed to monitor drought conditions. However, different drought indices have differing characteristics, and are suitable for specific environments. The aim of this study was to compare the occurrence of drought, as detected by remote sensing across the Continental United States (CONUS). We used drought events during 2011 and 2012 to compare various indices developed for the study of drought phenomena. Three in situ drought indices, the Palmer drought severity index (PDSI), Z-index and standardized precipitation indices (SPI) with different time scales were used to evaluate drought conditions in different climate divisions. The drought indices compared in this study include the vegetation condition index (VCI), the temperature condition index (TCI), the perpendicular drought index (PDI) and modified PDI (MPDI) derived from moderate-resolution imaging spectroradiometer (MODIS) data, the precipitation condition index (PCI) derived from tropical rainfall measuring mission (TRMM) data, and the soil moisture condition index (SMCI) derived from advanced microwave scanning radiometer - earth observing system (AMSR-E). Other synthesized drought indices, which combine indices such as VCI, TCI, SMCI and PCI, were also compared in this study. These included the vegetation health index (VHI), temperature vegetation dryness index (TVDI), scaled drought condition index (SDCI), the microwave integrated drought index (MIDI), the synthesized drought index (SDI), the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI). The results include a wide variety of drought conditions based on different drought indices. Meteorological drought indices indicated that more regions were under severe drought than agricultural drought indices did. Drought indices appear to be more similar to standard drought indices than PDI, MPDI and TVDI. The results also indicate that different indices have strengths and weaknesses in different climates across CONUS. SMCI has a good correlation with short-term SPI, and the sensitivity of SMCI is strongly dependent on terrain, as it performs worse in regions with heavy tree cover than in regions with a low density of vegetation. TCI, VCI, PDI and MPDI are more similar to 3-month SPI data, but correlate weakly with station-based indices located in areas of high precipitation, higher soil permeability, large-scale agriculture and forests. PCI is more strongly correlated with short-term drought conditions in almost all climate divisions than other single indices. VCI would be more reliable if there were only red and NIR surface reflectance bands available, while VHI would be a better choice if only NDVI and LST data were available. Condition index-based drought indices (PCI, TCI, VCI, SMCI, VHI, SDCI, SDCI, MIDI, OVDI, OMDI) performed better than other categories of drought indices, and the use of time series analysis may be a contributing factor to this difference in performance.