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- 摘要
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- 实验方案
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Classification of Urban Hyperspectral Remote Sensing Imagery Based on Optimized Spectral Angle Mapping
摘要： Hyperspectral remote sensing imagery provides highly precise spectral information. Thus, it is suitable for the land use classification of urban areas that are composed of complicated structures. In this study, a new spectral angle and vector mapping (SAVM) classification method, which adds a factor based on ''the differences in the spectral vector lengths'' among image pixels to the spectral angle mapping (SAM) classification method, is proposed. The SAM and SAVM methods were applied to classify the aerial hyperspectral digital imagery collection experiment imagery acquired from the business district of Washington, DC, USA. The results demonstrated that the overall classification accuracy of the SAM was 64.29%, with a Kappa coefficient of 0.57, while the overall classification accuracy of the SAVM was 81.06%, with a Kappa coefficient of 0.76. The overall classification accuracy was improved by 16.77% by the SAVM, indicating that the use of a SAVM classification method that considers both the spectral angle between the reference spectrum and the test spectrum and the differences in the spectral vector lengths among image pixels can improve the classification accuracy of urban area with hyperspectral remote sensing imagery.
关键词： Hyperspectral imagery,Spectral angle and vector mapping (SAVM),Classification,Spectral angle mapping (SAM)
更新于2025-09-23 15:23:52
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Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS)
摘要： Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps in Landsat data, using one year or less of data and without using other satellite data. Each gap pixel is filled by an alternative similar pixel that is located in a non-missing region of the image. The alternative similar pixel locations are identified by comparison of reflectance time series using a SAM metric revised to be adaptive to missing observations. A time series segmentation-and-clustering approach is used to increase the search efficiency. The SAMSTS algorithm is demonstrated using six months of Landsat 8 Operational Land Imager (OLI) reflectance time series over three 150 × 150 km (5000 × 5000 30 m pixels) areas in California, Minnesota and Kansas. The three areas contain different land cover types, especially crops that have different phenology and abrupt changes due to agricultural harvesting, which make gap filling challenging. Fillings on simulated gaps, which are equivalent to 36% of 5000 × 5000 images in each test area, are presented. The gap filling accuracy is assessed quantitatively, and the SAMSTS algorithm is shown to perform better than the simple closest temporal pixel substitution gap filling approach and the sinusoidal harmonic model-based gap filling approach. The SAMSTS algorithm provides gap-filled data with five-band reflective-wavelength root-mean-square differences less the 0.02, which is comparable to the OLI reflectance calibration accuracy.
关键词： Landsat,reflectance,time series,spectral angle mapper,gap filling
更新于2025-09-23 15:23:52
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Investigation of compositional variations in chromitite ore with imaging Laser Induced Breakdown Spectroscopy and Spectral Angle Mapper classification algorithm
摘要： This paper focusses on the applicability of the Spectral Angle Mapper (SAM) algorithm for supervised classification of imaging Laser Induced Breakdown Spectroscopy (LIBS) data. Our main objective is to investigate variations in the chemical/mineralogical composition of complex ore from the sub-millimetre to the metre scale, which may offer novel and barely investigated interpretation opportunities for exploration purposes. This research is based on coarse chromitite ore from Merensky Reef, represented by a drill core and a small section through the upper chromitite layer. Detailed LIBS-based imaging measurements were accompanied by space-resolved reference measurements based on SEM/MLA and EDXRF, as well as bulk chemical analyses for multiple core slices. The SAM algorithm was applied for classification of LIBS hyperspectral images with respect to differences in mineral chemistry. Our investigations focused on the pre-processing of LIBS spectra prior to SAM classification, on spectral library development, as well as on the validation of the classified data. The SAM classification algorithm, which is solely based on ratios between spectral intensities, was found insensitive to normal shot-to-shot plasma variations and to chemically induced matrix effects. However, the algorithm may become inaccurate at low signal to noise ratios, at the border between different mineral grains (mixed spectra), or when classifying chemically similar phases such as pyrite and pyrrhotite. The extent of mixed spectra depends both on the size of the mineral grains as well as on the spot size of the LIBS laser. The SAM algorithm was successfully applied for classification of several base metal sulphides, rock-forming minerals, accessory minerals, as well as several mixed phases representing the main borders between different mineral grains. The resulting classified LIBS image shows the spatial distribution of the different phases, which is in very good agreement with the space-resolved reference measurements based on EDXRF and SEM/MLA. The results also highlight the extremely heterogeneous distribution of e.g. the sulphide phases in the investigated core piece. The applicability of the LIBS-SAM classification image for estimating metal concentrations based on point counting has been explored for Cu, Ni, S, and Cr. We conclude that this approach, when applied on sufficiently large surfaces, enables semi-quantitative data analysis, as well as the possible detection of trace elements (e.g. Pt, Pd) that occur in very small nuggets.
关键词： Chromitite Ore,Core Scanner,Spectral Angle Mapper,Geochemical Exploration,Laser Induced Breakdown Spectroscopy (LIBS),Mineral Classification
更新于2025-09-12 10:27:22
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Applications and Challenges of Geospatial Technology (Potential and Future Trends) || Application of ASTER Remote Sensing for Lithological Mapping in the Udaipur District of Rajasthan, India
摘要： Remote sensing applications for earth studies such as lithological discrimination, geological mapping and potential mineral exploration have shown great success worldwide. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level-1B image includes visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands that have been analysed to discriminate lithology features in meta-sedimentary terrains of Aravalli Supergroup in Udaipur area of Rajasthan, India. The area comprises various types of geological settings and rock types composed of economic valuable deposits of lead, zinc, copper, micas and marbles; they show spectral reflectance distinctly in bands of VNIR and SWIR. The unique spectral signature reflected by lithological unit shows effectiveness in lithological mapping. The reflectance spectra of various rock types, namely, phyllitic dolomite, siliceous dolomite, metagreywacke, quartzite and gneiss, were collected in situ using spectroradiometer and used as reference of ASTER image for the preparation of spectral signature of different lithological units. The image is applied to analysis atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and empirical line calibration techniques to convert pixel radiance values into reflectance. A minimum noise fraction (MNF) transform is applied to identify the inherent variance of spectral reflectance and effectively discriminates various lithological units. The different types of lithological units are clearly discriminated using MNF method. Spectral Angle Mapper (SAM) classification is an effective tool for differentiating rock types and its distinct mineralogical composition from associated terrains. Spectral Angle Mapper (SAM) classification uses field-derived spectral signature to demarcate various lithological features with its spatial extent. The result shows different lithological units under Aravalli Supergroup, Banded Gneissic Complex and intrusive formations that are composed of meta-arkose, conglomerate, phyllite, mica schist, dolomite, metagreywacke and migmatites in various locations. The extracted geological features using ASTER image show strong resampling with the district resource map and validated using ground truth verification. The overall accuracy of SAM-classified map of lithological units is 73.39% and Kappa coefficient of 0.59. Mapping the lithological features using ASTER image, data coupled with MNF and SAM techniques provides relatively accurate result, and this study may be used for discrimination of lithological units with its spatial characteristics.
关键词： FLAASH,Lithological mapping,Spectral Angle Mapper,Remote sensing and GIS,ASTER,Minimum noise fraction
更新于2025-09-10 09:29:36
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Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape - A Case Study of Taita Hills, Kenya
摘要： Recent advances in hyperspectral remote sensing techniques and technologies allow us to more accurately identify larger range of crop species from airborne measurements. This study employs hyperspectral AISA Eagle VNIR imagery acquired with 9 nm spectral and 0.6 m spatial resolutions over a spectral range of 400 nm to 1000 nm. The area of study is the Taita hills in Kenya. Various crops are grown in this region basically for food and as an economic activity. The crops addressed are: maize, bananas, avocados, and sugarcane and mango trees. The main objectives of this study were to study what crop species can be distinguished from the cultivated population crops in the agricultural landscape and what feature space discriminates most effectively the spectral signatures of different species. Spectral Angle Mapper (SAM) algorithm together with some dissimilarity concepts was applied in this work. The spectral signatures for crops were collected using accurate field plot maps. Accuracy assessment was done using independent training vector data. We achieved an overall accuracy of 77% with a kappa value of 0.67. Various crops in different locations were identified and shown.
关键词： Spectral angle mapper,Hyperspectral imaging,Spectral signatures,Spectral variation,Crop identification
更新于2025-09-09 09:28:46
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Difference-based target detection using Mahalanobis distance and spectral angle
摘要： Two difference-based target detection methods are proposed in this work. In contrast to many target detectors which only calculate the distance between the testing pixel to the target spectrum, the proposed methods calculate the distance of the testing pixel to both of target and of background spectra. In other words, they utilize the difference between target and background computed distances. The first proposed method uses the Mahalanobis distance and benefits the valuable information contained in the statistics of targets and background. The second proposed method uses the kernel-based spectral angle mapper to benefit the advantages of spectral angle and kernel trick to separate targets from background, especially in non-linear cases. The experiments done on three real hyperspectral images indicate the high detection probability of the proposed methods compared to several target detectors.
关键词： hyperspectral imaging,Mahalanobis distance,target detection,spectral angle
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Feature-Level Loss for Multispectral Pan-Sharpening with Machine Learning
摘要： Multispectral pan-sharpening plays an important role in providing earth observation with both high-spatial and high-spectral resolutions, and recently pan-sharpening with machine learning has been attracting broad interest. However, these algorithms minimizing the pixel-wise mean squared error, generally suffer from over-smoothed results that lack of high-frequency details in both spatial and spectral dimensions. In this paper, we propose to tackle this problem by shifting the learning loss from pixel-wise error to a higher-level feature loss. The new loss function, formulated by spatial structure similarity and spectral angle mapping, pushes the model to generate results that have similar feature representations with ground truth, rather than match with pixel-wise accuracy. Consequently, more realistic fusion results can be produced. Visual and quantitative analysis both demonstrate that our approach achieves better performance in comparison with state-of-the-art algorithms. Furthermore, experiments on high-level remote sensing task further confirm the superiority of the proposed method in real applications.
关键词： Spectral Angle Mapping,Spatial Structure Similarity,Multispectral Pan-sharpening,Feature-Level Loss,Machine Learning
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fuzzy Fusion of Change Vector Analysis and Spectral Angle Mapper for Hyperspectral Change Detection
摘要： Change Vector Analysis (CVA) is one of the most widely used approaches for change detection in multispectral and hyperspectral images. Although, in CVA, the spectral change vector (CV) comprises the angle as well as the magnitude of the change, typically only the magnitude measure is used as change criterion. On the other hand, the spectral angle mapper (SAM) uses only the angle measure as criterion for change detection. It is envisaged that combining the angle and magnitude for change detection (i.e. combining SAM and magnitude CVA) can improve the change detection performance, yet only a limited number of approaches have been proposed in the literature so far. This paper presents a novel fuzzy inference combination strategy that combines the angle and magnitude distances, referred to as Fuzzy CVA (FuzCVA), and is shown that the proposed approach can provide improved change detection performance by effectively combining magnitude and angle measures.
关键词： Hyperspectral Imaging,change detection,spectral angle mapper,fuzzy inference,change vector analysis
更新于2025-09-04 15:30:14