scholarly journals Lithological mapping of East Tianshan area using integrated data fused by Chinese GF-1 PAN and ASTER multi-spectral data

2018 ◽  
Vol 10 (1) ◽  
pp. 532-543 ◽  
Author(s):  
Min Yang ◽  
Lei Kang ◽  
Huaqing Chen ◽  
Min Zhou ◽  
Jianghua Zhang
Keyword(s):  
Spectral Resolution ◽  
Near Infrared ◽  
Thermal Emission ◽  
Low Cost ◽  
Short Wave ◽  
Support Vector ◽  
Tianshan Mountain ◽  
Multispectral Data ◽  
Fusion Data ◽  
East Tianshan

Abstract The East Tianshan Mountain is one of the most important gold ore forming zones in northwestern China and central Asia. The Chinese GaoFen-1 (GF-1), the first Chinese high resolution satellite, is characterized by its 2-m resolution PAN data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the well-known earth observation satellite, is advanced by its finer spectral resolution owing 9 bands in the visible and near infrared (VNIR) to the short-wave infrared (SWIR) region. In this study, we fused the GF-1 PAN and the ASTER multispectral data using the well-known Gram-Schmidt Pan Sharpening (G-S) method to produce a new data with both high spatial and spectral resolution. Then different lithological units were mapped respectively using the fusion data, the ASTER data and the WorldView-3 data by support vector machine (SVM) method. In order to assess this fusion data, a comparison work was executed among the three mapping results. The comparison work indicated that lithological classification using the new fusion data is an efficient, robust and low cost method, and it could replace the WV-3 data in some large sale geological work.

scholarly journals Using aster multispectral imagery for mapping woody invasive species in pico da vara natural reserve (Azores Islands, Portugal)

2014 ◽  
Vol 38 (3) ◽  
pp. 391-401 ◽  
Author(s):  
Artur Gil ◽  
Qian Yu ◽  
Mohamed Abadi ◽  
Helena Calado
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Low Cost ◽  
Woody Species ◽  
Short Wave ◽  
Support Vector ◽  
Continuous Assessment ◽  
K Nearest Neighbor ◽  
Azores Islands ◽  
Natural Reserve

This paper aims to assess the effectiveness of ASTER imagery to support the mapping of Pittosporum undulatum, an invasive woody species, in Pico da Vara Natural Reserve (S. Miguel Island, Archipelago of the Azores, Portugal). This assessment was done by applying K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood (MLC) pixel-based supervised classifications to 4 different geographic and remote sensing datasets constituted by the Visible, Near-Infrared (VNIR) and Short Wave Infrared (SWIR) of the ASTER sensor and by digital cartography associated to orography (altitude and "distance to water streams") of which the spatial distribution of Pittosporum undulatum directly depends. Overall, most performed classifications showed a strong agreement and high accuracy. At targeted species level, the two higher classification accuracies were obtained when applying MLC and KNN to the VNIR bands coupled with auxiliary geographic information use. Results improved significantly by including ecology and occurrence information of species (altitude and distance to water streams) in the classification scheme. These results show that the use of ASTER sensor VNIR spectral bands, when coupled to relevant ancillary GIS data, can constitute an effective and low cost approach for the evaluation and continuous assessment of Pittosporum undulatum woodland propagation and distribution within Protected Areas of the Azores Islands.

scholarly journals Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

2019 ◽  
Vol 11 (12) ◽  
pp. 1416 ◽  
Author(s):  
Michaela De Giglio ◽  
Nicolas Greggio ◽  
Floriano Goffo ◽  
Nicola Merloni ◽  
Marco Dubbini ◽  
...  
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Coastal Dunes ◽  
Level Of Detail ◽  
Support Vector ◽  
Final Decision ◽  
Dune Vegetation ◽  
Vegetation Communities ◽  
The North ◽  
Object Based

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

scholarly journals Applying spectral analysis for identification of alteration zones in north Saveh area, Iran

2020 ◽  
Author(s):  
Kazem Rangzan ◽  
Somayeh Beyranvand ◽  
Hoshang Pourkaseb ◽  
Hojjatollah Ranjbar ◽  
Alireza Zarasvandi
Keyword(s):  
Hydrothermal Alteration ◽  
Near Infrared ◽  
Thermal Emission ◽  
Spectral Response ◽  
Volcanic Rocks ◽  
Short Wave ◽  
Rapid Identification ◽  
Spectral Angle Mapper ◽  
Alteration Zones ◽  
The North

An extensive series of volcanic rocks are exposed in the north of Saveh city, Iran, which consist of phyllic, argillic and propylitic hydrothermal alteration types. For the purpose of the investigation, a FieldSpec3® spectroradiometer was used to measure the spectral response of the mineral content of these rocks. The spectral analyses of reflectance curve by The Spectral Geologist (TSG) software could discriminate kaolinite and montmorillonite (argillic), illite, muscovite, phengite and paragonite (phyllic), hornblende and chlorite, siderite (propylitic), hematite and goethite from the gossans. It also detected gypsum of hydrothermal alteration zones. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image, which was used for mapping the hydrothermal alteration minerals, contains the Visible and Near Infrared (VNIR) wavelengths between 0.52 µm and 0.86 µm, Short Wave Infrared (SWIR) wavelengths between 1.6 µm and 2.43 µm and Thermal Infrared (TIR) wavelengths between 8.125 µm and 11.65 µm with 15, 30 and 90 m spatial resolutions, respectively. For calibration of the ASTER images, the extracted spectra of different rocks and minerals were used for atmospheric and radiometric corrections. Mixture tuned matched filtering (MTMF) and Spectral Angle Mapper (SAM) were applied on ASTER data to map the hydrothermal alteration of minerals. The use of the spectroradiometry techniques in conjunction with other data exhibits the ability of these new methods for non-destructive and rapid identification of mineral types for more detailed investigation. The results show that the area has undergone different levels of hydrothermal alteration, so much so that phyllic, argillic and propylitic types of hydrothermal alteration are present in the study area. This may point to high potential and promising zones for the exploration of porphyry mineralisation.

scholarly journals SHIP DETECTION BASED ON MULTIPLE FEATURES IN RANDOM FOREST MODEL FOR HYPERSPECTRAL IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 891-895
Author(s):  
N. Li ◽  
L. Ding ◽  
H. Zhao ◽  
J. Shi ◽  
D. Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Near Infrared ◽  
Texture Features ◽  
Short Wave ◽  
Hyperspectral Images ◽  
Support Vector ◽  
High Signal ◽  
Detection Accuracy ◽  
Multiple Features ◽  
Single Feature

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

CWT-Support Vector Regression Model and Its Application

2010 ◽  
Vol 113-116 ◽  
pp. 207-210
Author(s):  
Jie Fang Liu ◽  
Pu Mei Gao ◽  
Bao Lin Ma
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Support Vector ◽  
Traditional Learning ◽  
Risk Minimization ◽  
Support Vector Regression Model ◽  
Analytical Technique ◽  
Learning Machines ◽  
Empirical Risk ◽  
Svm Model

Near-infrared spectroscopy (NIR) analytical technique is simple, fast and low cost, making neither pollution nor damage to the samples, and can determine many components simultaneously. Continuous wavelet transform (CWT), as an application direction of the wavelet analysis, is keener to the signal slight change. Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. In this paper, we use CWT- SVM model to predict meat’s component. Compared with Partial Least Squares (PLS) and SVR, we get more satisfactory result.

Demonstration of the applicability of visible and near-infrared spatially resolved spectroscopy for rapid and nondestructive wood classification

Holzforschung ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Te Ma ◽  
Tetsuya Inagaki ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Short Wave ◽  
Physical Structure ◽  
Support Vector ◽  
Spatially Resolved ◽  
Standard Normal Variate ◽  
Spatially Resolved Spectroscopy

AbstractAlthough visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600–1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.

scholarly journals Identification of Phyllosilicates in the Antarctic Environment Using ASTER Satellite Data: Case Study from the Mesa Range, Campbell and Priestley Glaciers, Northern Victoria Land

2020 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Amin Beiranvand Pour ◽  
Milad Sekandari ◽  
Omeid Rahmani ◽  
Laura Crispini ◽  
Andreas Läufer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Thermal Emission ◽  
Ground Truth ◽  
Xrd Analysis ◽  
Short Wave ◽  
Metamorphic Complex ◽  
Victoria Land ◽  
The Antarctic ◽  
Antarctic Environment

In Antarctica, spectral mapping of altered minerals is very challenging due to the remoteness and inaccessibility of poorly exposed outcrops. This investigation evaluates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing imagery for mapping and discrimination of phyllosilicate mineral groups in the Antarctic environment of northern Victoria Land. The Mixture-Tuned Matched-Filtering (MTMF) and Constrained Energy Minimization (CEM) algorithms were used to detect the sub-pixel abundance of Al-rich, Fe3+-rich, Fe2+-rich and Mg-rich phyllosilicates using the visible and near-infrared (VNIR), short-wave infrared (SWIR) and thermal-infrared (TIR) bands of ASTER. Results indicate that Al-rich phyllosilicates are strongly detected in the exposed outcrops of the Granite Harbour granitoids, Wilson Metamorphic Complex and the Beacon Supergroup. The presence of the smectite mineral group derived from the Jurassic basaltic rocks (Ferrar Dolerite and Kirkpatrick Basalts) by weathering and decomposition processes implicates Fe3+-rich and Fe2+-rich phyllosilicates. Biotite (Fe2+-rich phyllosilicate) is detected associated with the Granite Harbour granitoids, Wilson Metamorphic Complex and Melbourne Volcanics. Mg-rich phyllosilicates are mostly mapped in the scree, glacial drift, moraine and crevasse fields derived from weathering and decomposition of the Kirkpatrick Basalt and Ferrar Dolerite. Chlorite (Mg-rich phyllosilicate) was generally mapped in the exposures of Granite Harbour granodiorite and granite and partially identified in the Ferrar Dolerite, the Kirkpatrick Basalt, the Priestley Formation and Priestley Schist and the scree, glacial drift and moraine. Statistical results indicate that Al-rich phyllosilicates class pixels are strongly discriminated, while the pixels attributed to Fe3+-rich class, Fe2+-rich and Mg-rich phyllosilicates classes contain some spectral mixing due to their subtle spectral differences in the VNIR+SWIR bands of ASTER. Results derived from TIR bands of ASTER show that a high level of confusion is associated with mafic phyllosilicates pixels (Fe3+-rich, Fe2+-rich and Mg-rich classes), whereas felsic phyllosilicates (Al-rich class) pixels are well mapped. Ground truth with detailed geological data, petrographic study and X-ray diffraction (XRD) analysis verified the remote sensing results. Consequently, ASTER image-map of phyllosilicate minerals is generated for the Mesa Range, Campbell and Priestley Glaciers, northern Victoria Land of Antarctica.

Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine

2021 ◽  
pp. 000370282199965
Author(s):  
Yusuke Hattori ◽  
Yuka Hoshi ◽  
Yasunori Ichimura ◽  
Yasuo Sugiura ◽  
Makoto Otsuka
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Low Cost ◽  
Discrimination Performance ◽  
Principal Component ◽  
Support Vector ◽  
Reference Library ◽  
Infrared Spectrophotometer

The objective of this work is to demonstrate the potential of near-infrared spectroscopy for common screening of falsified medicines in the field by means of a device-independent universal discrimination approach. In order to provide a useful discrimination tool to protect people from low-quality medical products, not only is a low-cost and portable screening device necessary, but a reference library is also essential. The authors believe that a device-dependent reference library inhibits near-infrared spectroscopy from becoming a popular screening tool. In this study, to develop a device-independent method, discrimination performance is evaluated using different devices for training and testing. The training data sets for the reference library were prepared using a bench-top Fourier transform near-infrared spectrophotometer, and predictive discrimination was performed using the spectral data by a low-cost and portable wavelength dispersive near-infrared spectrophotometer. A near-infrared spectrum-based support vector machine was used for these purposes, but the screening resulted in low accuracy thought to be caused by the intrinsically device-dependent features of the spectra data. Thus, principal component analysis was performed to collect the proper components to discriminate low-quality products from standard products. The principal component score-based support vector machine was able to produce highly accurate results, identifying falsified products with no false positive cases.

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