Spectral Angle Method Research Articles

APPLICATION OF MULTISPECTRAL IMAGES TO SEARCH FOR CONSTRUCTION OBJECTS ON THE SPECTRAL SIGNATURES BASE

The work is devoted to the study of Landsat-8 multispectral images of not high resolution using the spectral angle method on the base of spectral signatures libraries to detect objects under construction in an urban area. The physical basis of the research method is that all objects have different reflection coefficients depending on the wavelength. This property makes it possible to identify various substances by their spectral signatures. In the work, an automatic comparison of the curves of the spectral reflectivity of objects on a low resolution space multispectral image was made to identify the identity of the characteristic energy absorption and reflection zones for detecting objects in the construction process. The article also describes the stages of image preprocessing, cross-track illumination correction of the image, atmospheric correction, and mathematical operations of bands transformation, which provide more opportunities for analysis and recognition of objects using a spectral study of a space image. The study accurately determines the presence or absence of the desired materials, since the search is based on the molecular structure of the substance. Also, the use of multispectral images allows you to analyze the entire city at the same time. The initial data was taken from a 2021 Landsat-8 satellite image with 11 bands, with a resolution of 30 meters, which was enhanced to 15 meters during pre-processing. The results of the search and detection of objects under construction in the city are given. The detection results can be used as input data for further in-depth analysis.

Sparse Tensor Model-Based Spectral Angle Detector for Hyperspectral Target Detection

In recent years, the hyperspectral target detection technique has received widespread attention, and various methods have been proposed. However, most of these methods suffer mainly from two problems. First, the methods perform detection based on the original image, and thus, the targets are easily contaminated by the complex background. Second, a large amount of prior information is usually required, but difficult to obtain. To solve these problems, we proposed a Sparse Tensor model-based Spectral Angle (STSA) detector for hyperspectral target detection. First, with a tensor decomposition model, we obtained a sparse tensor component separated from the original image. Based on the sparse tensor, the detection is less contaminated by the background, and the sparse tensor retains the spatial structure using the developed 3D tensor-based decomposition model. Second, to further enhance the performance, the projected spectral angle method was developed, which only requires a single target spectrum as prior information. Finally, to increase the separation ability between the target and background, we applied a statistical strategy to highlight the target and suppress the background. The experiments were carried out based on four public hyperspectral datasets. The results showed that the proposed STSA method is more accurate than 11 benchmark methods.

Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images

Image segmentation is the decisive process in object-based image analysis, but segmenting a landscape scene into meaningful geo-objects remains a challenge. In recent years, there has been growing interest in hybrid methods that combine initial segmentation with subsequent region merging, because they exploit spectral signals across an entire geo-object, including both the boundary signals used to delineate the initial segments, and the interior signals used to merge similar segments. However, existing algorithms commonly use a single, global parameter to control the process of region merging, thereby limiting the goodness-of-fit between segments and geo-objects, since homogeneous and heterogeneous segments are treated equally. To overcome this limitation, we developed a new hybrid segmentation method that employs local spectral angle (SA) thresholds for region merging. We implemented our local SA method in three very different landscapes, then compared our region merging method to the global SA method, as well as the global elevation method used in System for Automated Geoscientific Analyses (SAGA). In all three landscapes, the results revealed that the local SA segmentation provides a better fit to reference polygons than the two global threshold methods, as measured using a modified discrepancy measure for the purpose of geo-object recognition (QRM). We also found that the local SA method produced segments with a greater variation in size, indicating the method is effective for achieving multi-scale segmentation.

Extracting mineral alteration information using WorldView-3 data

The multispectral commercial satellite-WorldView-3 (WV-3) has the highest spatial, spectral and radiation resolutions among the satellites currently and can generate good data in the shortwave infrared (SWIR). The study area is located in the Pobei area of Xinjiang Uygur Autonomous Region, which is rich in mineral resources. The spectral analyses of some typical altered hydroxy-bearing, iron-bearing, and carbonate-bearing minerals could establish several Principal Component Analysis (PCA) models and mineral indices, using the visible and near infrared (VNIR) and the shortwave infrared (SWIR) subsystems of WV-3 data. In addition, the Spectral Angle Method and the spectrum index tool of ENVI software were used to extract the relevant alteration information. The WV-3 data is well suited for identifying hydroxy-bearing alteration with rich SWIR bands which distinguish Al-OH-bearing from Mg-OH-bearing alteration. Hence, this study provides a basis for the prediction of mineral resources in the Pobei area and sets the foundation for WV-3 data to be used as an important tool in extracting alteration information and prospecting practices.

A method for recognising building materials based on hyperspectral remote sensing

Information about building materials plays an important role in the evaluation of military targets strikes, urban planning, disaster assessment and natural disaster compensation. This study aimed to investigate the information about building materials from hyperspectral remote sensing images based on spectral measurements. Additionally, we analysed the spectral characteristics of common building materials. Based on the spectral library and spectra measured, the building material types were identified in urban areas using the Spectral Angle Method and Spectral Information Divergence method. The information about building material types that could be extracted included the artificial paint, pine, concrete and asphalt shingles. The experimental results showed that hyperspectral remote sensing technology can extract information about building materials and that the Spectral Angle Method is more reliable than Spectral Information Divergence Method.

Lineament Tectonics and Mineralizatin in Tarom Area, North Iran

The study area that is part of Tarom volcano-plutonic zone which host many hydrothermal base metal deposits located in Zanjan, NW Iran. Understanding the tectonic events that can cause mineralization and hydrothermal alteration are significant factor in assessing the exploration potential of different structures. In this research, hydrothermal alteration such as Iron oxide, argillic, phyllic, and propylitic zones were determined by Spectral Angle Method (SAM) and also lineaments identified by high pass filters and hill-shade DEM techniques on Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) data. Field studies revealed most alteration and mineralization occurred in NE-SW fractures and control mineralization.

REMOTE SENSING ANOMALY EXTRACTION IN IMPORTANT METALLOGENIC BELTS OF WESTERN CHINA

Altered rock information extraction is one of the important topics in the remote sensing exploration. This paper has proposed a functional technological process, which includes data preparation, information enhancement, thresholding and mapping. On the basis of a study of such means as principal component analysis, band ratio, spectral angle method, this process can surely play an effective role in the prospecting work within important metallogenic belts of western China. Some major problems in current work of this aspect are also discussed.

Optimal classification methods for mapping agricultural tillage practices

The classification of agricultural tillage systems has proven challenging in the past using traditional classification methods due to the similarity of spectral reflectance signatures of soils and senescent crop residues. In this study, five classification methods were examined to determine the most suitable classification algorithm for the identification of no-till (NT) and traditional tillage (TT) cropping methods: minimum distance (MD), Mahalanobis distance, Maximum Likelihood (ML), spectral angle mapping (SAM), and the cosine of the angle concept (CAC). A Landsat ETM+ image acquired over southern Michigan and northern Indiana was used to test these classification methods. Each classification method was validated with 293 ground truth sampling locations collected commensurate with the satellite overpass. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance. The results indicate that of the classification routines examined, the two spectral angle methods were superior to the others. The cosine of the angle concept algorithm outperformed all the other classification routines for tillage practice identification and mapping, yielding an overall accuracy of 97.2% (Kappa=0.959).