Mapper Algorithm Research Articles

Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging, EO-1 Hyperion sensor

Applied in Djebel Meni (Northwestern of Algeria), this research highlights the results obtained from the supervised classification using the Spectral Angle Mapper (SAM) algorithm, through introducing the spectral signatures of illite, kaolinite, and montmorillonite, via Jet Propulsion Laboratory (JPL) spectral library. These results were compared to the ones of the SAM classification, which use spectral signatures obtained by the Sequential Maximum Angle Convex Cone (SMACC) endmembers extraction algorithm. This implies the ability to detect and identify any object present on the Earth’s surface, whether its nature is mineral, vegetal, or human made, from hyperspectral imaging. By extracting the spectral signatures with the SMACC algorithm and matching them to the current signatures of JPL spectral library, comparing spectral signatures with another is not an easy task. Indeed, for a better comparison and a more appropriate interpretation in the use of the SAM classification, the results obtained were very relatively convincing because, regarding very strong similarities. It appears also that the signatures extracted with SMACC occupy the same areas as those of the JPL spectral library. This method of detection and identification of any present object on the Earth’s surface is rather conclusive.

Identification of key features using topological data analysis for accurate prediction of manufacturing system outputs

Topological data analysis (TDA) has emerged as one of the most promising approaches to extract insights from high-dimensional data of varying types such as images, point clouds, and meshes, in an unsupervised manner. To the best of our knowledge, here, we provide the first successful application of TDA in the manufacturing systems domain. We apply a widely used TDA method, known as the Mapper algorithm, on two benchmark data sets for chemical process yield prediction and semiconductor wafer fault detection, respectively. The algorithm yields topological networks that capture the intrinsic clusters and connections among the clusters present in the data sets, which are difficult to detect using traditional methods. We select key process variables or features that impact the system outcomes by analyzing the network shapes. We then use predictive models to evaluate the impact of the selected features. Results show that the models achieve at least the same level of high prediction accuracy as with all the process variables, thereby, providing a way to carry out process monitoring and control in a more cost-effective manner.

A simple indicator for estimating the noise level of a hyperspectral data cube for earth observation missions

A method for estimating the level of noise of a hyperspectral data cube is proposed. The method includes noise reduction using the Minimum Noise Fraction (MNF) transform and mapping the spectral dissimilarity between the pixels in the resulting noise-reduced radiance image, as well as in the original noisy radiance image, using the Spectral Angle Mapper (SAM) algorithm. Comparing the two maps, on a pixel by pixel basis, gives a value indicating the addition of noise to the spectrum of each pixel. An average value for the entire image is calculated, defined as the Image Noise Indicator (INI). In practice, this value indicates the quality of the data. Combining the INI value with the level of radiance enables estimating the Image Noise Level (INL). The method was applied and examined on a noisy synthetic image and then implemented for over 20 acquired images from different hyperspectral sensors and their noise level was estimated. Further examination showed that the INI value is independent of the heterogeneity of the hyperspectral data cube. The INI value, indicating the noise level, might increase as a geo-reference procedure is applied to hyperspectral data cube. The INI–INL can also be used as a quality indicator (QI) in the ongoing effort to objectively certify hyperspectral remote sensing images for practical usages.

Mapping advanced argillic alteration zones with ASTER and Hyperion data in the Andes Mountains of Peru

This study evaluates Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion hyperspectral sensor datasets to detect advanced argillic minerals. The spectral signatures of some alteration clay minerals, such as dickite and alunite, have similar absorption features; thus separating them using multispectral satellite images is a complex challenge. However, Hyperion with its fine spectral bands has potential for good separability of features. The Spectral Angle Mapper algorithm was used in this study to map three advanced argillic alteration minerals (alunite, kaolinite, and dickite) in a known alteration zone in the Peruvian Andes. The results from ASTER and Hyperion were analyzed, compared, and validated using a Portable Infrared Mineral Analyzer field spectrometer. The alterations corresponding to kaolinite and alunite were detected with both ASTER and Hyperion (80% to 84% accuracy). However, the dickite mineral was identified only with Hyperion (82% accuracy).

Lipid volume fraction in atherosclerotic plaque phantoms classified under saline conditions by multispectral angioscopy at near-infrared wavelengths around 1200 nm.

To identify high-risk atherosclerotic lesions, we require detailed information on the stability of atherosclerotic plaques. In this study, we quantitatively classified the lipid volume fractions in atherosclerotic plaque phantoms by a novel angioscope combined with near-infrared multispectral imaging. The multispectral angioscope was operated at peak absorption wavelengths of lipid in vulnerable plaques (1150, 1200, and 1300 nm) and at lower absorption wavelengths of water. The potential of the multispectral angioscope was demonstrated in atherosclerotic plaque phantoms containing 10-60 vol.% lipid and immersed in saline solution. The acquired multispectral data were processed by a spectral angle mapper algorithm, which enhanced the simulated plaque areas. Consequently, we classified the lipid volume fractions into five categories (0-5, 5-15, 15-30, 30-50, and 50-60 vol.%). Multispectral angioscopy at wavelengths around 1200 nm is a powerful tool for quantitatively evaluating the stability of atherosclerotic plaques based on the lipid volume fractions.

Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image

Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.

Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

Towards a Standard Plant Species Spectral Library Protocol for Vegetation Mapping: A Case Study in the Shrubland of Doñana National Park

One of the main applications of field spectroscopy is the generation of spectral libraries of Earth’s surfaces or materials to support mapping activities using imaging spectroscopy. To enhance the reliability of these libraries, spectral signature acquisition should be carried out following standard procedures and controlled experimental approaches. This paper presents a standard protocol for the creation of a spectral library for plant species. The protocol is based on characterizing the reflectance spectral response of different species in the spatiotemporal domain, by accounting for intra-species variation and inter-species similarity. A practical case study was conducted on the shrubland located in Doñana National Park (SW Spain). Spectral libraries of the five dominant shrub species were built (Erica scoparia, Halimium halimifolium, Ulex australis, Rosmarinus officinalis, and Stauracanthus genistoides). An estimation was made of the separability between species: on one hand, the Student’s t-test evaluates significant intra-species variability (p < 0.05) and on the other hand, spectral similarity value (SSV) and spectral angle mapper (SAM) algorithms obtain significant separability values for dominant species, although it was not possible to discriminate the legume species Ulex australis and Stauracanthus genistoides.

Mineral Mapping of Lunar Highland Region using Moon Mineralogy Mapper (M3) Hyperspectral Data

Abstract: Mineral mapping of lunar surface is significant to understand the origin, crustal evolution, geological history and stratigraphy of the Moon. Advancements in orbital satellite sensor technology has allowed discriminating the minerals on lunar surface using hyperspectral data. The Moon Mineralogy Mapper (M3) onboard Chandrayaan-1 provides unprecedented data of lunar surface to study about the Moon. In this paper, the minerals in and around Wegener crater on the lunar highland region was investigated using M3 data. For this purpose, we used spectral similarity mapping (SSM) and band shape methods to discriminate minerals in this region. In SSM, dimensionality of M3 data is reduced using Maximum Noise Fraction (MNF) and spectral end-members are extracted through Pixel Purity Index (PPI) algorithm. The mineralogical diversity is classified using Spectral Angle Mapper (SAM) algorithm. In addition, different band shape algorithms such as band curvature (bc), band strength (bs), band tilt (bt) and band ratio (br) at crucial wavelengths are applied to recognise the minerals. Low-Ca pyroxene (LCP) minerals are identified in and around this region. The presence of LCP minerals may be attributed to magmatic differentiation or later stage layered mafic intrusions.

Spectral Uncertainty of Terrestrial Objects and the Applicability of Spectral Angle Mapper Algorithm

The spectral uncertainty of terrestrial objects causes a certain degree of spectral differences among feature spectra, which affects the accuracy of object recognition and also impacts the object recognition of spectral angle mapper algorithm (SAM). The spectral angle mapper algorithm is based on the overall similarity of the spectral curves, which was widely used in the classification of hyperspectral remotely sensed information. The spectral angle mapper algorithm does not take the spectral uncertainty of terrestrial objects into account while calculating the spectral angle between the spectral curves, and therefore does not tend to correctly identify the target objects. The applicability of the spectral angle mapper algorithm is studied for the spectral uncertainty of terrestrial objects and a modified SAM is proposed in this paper. In order to overcome the influence of the spectral uncertainty, the basic idea is to set a spectral difference value for the test spectra and the reference spectra and to calculate the spectral difference value based on derivation method according to the principle of minimum angle between the test spectra and the reference spectra. By considering the impact of the spectral uncertainty of terrestrial objects, this paper uses five kaolinite mineral spectra of USGS to calculate the spectral angle between the five kalinite mineral spectra by using local band combination and all bands to verify the improved algorithm. The calculation results and the applicability of the spectral angle mapper algorithm were analyzed. The results obtained from the experiments based on USGS mineral spectral data indicate that the modified SAM is not only helpful in characterizing and overcoming the impact of the spectral uncertainty but it can also improve the accuracy of object recognition to certain extent especially for selecting local band combination and has better applicability for the spectral uncertainty of terrestrial objects.

A Landsat-based study of black rock coatings proximal to base metal smelters, Sudbury, Ontario, Canada

Past emission of metal-bearing particulate matter, sulphur dioxide (SO2), and sulphuric acid by base metal smelters in the Sudbury region led to widespread loss of vegetation, contamination of soils, and formation of black coatings on rock surfaces. These black coatings formed through the incorporation of smelter-borne particulate matter into the partly dissolved uppermost layers of siliceous minerals on exposed rock, and are characterized by high heavy-metal content. This study involved assessment of the reflectance properties of black coatings in the Sudbury region, and determination of the geographic distribution of coatings through supervised classification of reflectance data derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Classifications involved the use of the Spectral Angle Mapper (SAM), Maximum Likelihood, and Feedforward Backpropagation Neural Network algorithms. The reflectance spectra of black coatings in the Sudbury region are relatively flat and featureless, and are characterized by reflectance values less than ~13% across the visible, near-infrared, and short-wave infrared. Spectral properties are similar to those of magnetite, a spinel-group mineral known to be present in Sudbury coatings. The presence of carbon-rich soot particles may be an important influence on the reflectance properties of coatings. SAM classification results are characterized by the widespread mislabelling of uncoated urban and open-pit sites as mantled by black coatings, and neural network results problematically mislabel some uncoated wetland sites as coated. Results generated by the Maximum Likelihood algorithm most usefully depict the distribution of exposed black coatings in the Sudbury region. The mapping of black coatings using remote-sensing methods can provide useful information on the spatial character of environmental degradation in the vicinity of smelters, and should be helpful in the monitoring of environmental recovery where emissions have been reduced or eliminated.

Skin cells segmentation algorithm based on spectral angle and distance score

Abstract In the diagnosis of skin diseases by analyzing histopathological images of skin sections, the automated segmentation of cells in the epidermis area is an important step. Light microscopy based traditional methods usually cannot generate satisfying segmentation results due to complicated skin structures and limited information of this kind of image. In this study, we use a molecular hyperspectral imaging system to observe skin sections and propose a spectral based algorithm to segment epithelial cells. Unlike pixel-wise segmentation methods, the proposed algorithm considers both the spectral angle and the distance score between the test and the reference spectrum for segmentation. The experimental results indicate that the proposed algorithm performs better than the K-Means, fuzzy C-means, and spectral angle mapper algorithms because it can identify pixels with similar spectral angle but a different spectral distance.

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

Groundwater of Abu Dhabi Emirate: a regional assessment by means of remote sensing and geographic information system

Mapping of geological, topographical, and hydrological elements is critical for understanding and assessing the regional hydrological setting in an arid region. In this study, a synergistic approach has been developed, which uses a combination of remote sensing data and geographic information system (GIS) to map factors controlling groundwater recharge, discharge, and quality across the Abu Dhabi Emirate. The Spectral Angel Mapper (SAM) algorithm, which uses a n-D angle to match the pixels to reference spectra, was used to map new water-bearing rocks, and the deterministic eight-node (D8) algorithm, which allows flow to only one of the eight neighbors based on the direction of steepest descent, was used to map paleochannels. The terrain category was applied to simulate seawater intrusion from digital elevation model (DEM). New maps of lithology, normalized difference vegetation index (NDVI), and paleochannels were derived and interpreted from multi-sources of remote sensing data. The study indicated that the area was produced by a fluvial and eolian process and recharged by local, intermediate, and regional flows. The results showed that the Oman and Hafeet Mountains are the natural sources of groundwater recharge as well as HCO3, Ca, Na, and Mg in groundwater. The mapped factors were spatially correlated with hydrologic anomalies observed in groundwater wells. The integrated approach is timely, cost-effective, and can be used in arid regions for numerical modeling as well as water balance analysis.

Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data

The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.

Retrieval of soil physicochemical properties towards assessing salt-affected soils using Hyperspectral Data

The relationship between soil salinity parameters and their influence on soil spectral characteristics were analyzed using both satellite data (Hyperion) and reflectance data of soil samples collected from parts of Ahmedabad district of Gujarat, India. The soil spectral reflectance curves were assessed using absorption feature parameters by DISPEC software to identify suitable spectral band for salinity characterization. The Hyperion data of the study area were processed and classified into different classes by spectral angle mapper algorithm using spectral library generated from soil spectra. The results showed that among all the observed soil parameters Electrical Conductivity, Exchangeable Sodium Percentage, Cation Exchange Capacity and Mg++ predictions can be made accurately based on partial least square regression models developed from selected wavelengths. Out of the total study area moderately saline-sodic, severely saline-sodic, severely saline and slightly saline soils occupy 23.5, 12.6, 10.9 and 0.04%, respectively.

Mapping Agewise Discrimination of Arecanut Crop Water Requirement Using Hyperspectral Remote Sensing

Arecanut is one of the major plantation crops in India. The crop is grown extensively in many regions of the country due to its high commercial value. A part of Channageri taluk of Davanagere district, Karnataka, known as “the land of arecanut” is chosen for the study. The majority of the study area consists of arecanut plantations of various age groups. Growth stage is one of the major factors that govern the crop water requirement (CWR), which in turn influences irrigation planning and scheduling. Accurate estimation of crop area, area under different age groups and area under stress are extremely important to estimate CWR. Traditional methods of area estimation are costly and time consuming. With the advancement in hyperspectral remote sensing, within crop variations can be discriminated with enhanced reliability and accuracy. Using ASD FieldSpec Pro® handheld spectroradiometer spectral signatures of different age groups and stressed arecanut crops are collected to build spectral library. Using the spectral library and Hyperion imagery, arecanut crops of various age groups and under stress (crown choke disease) are mapped by Spectral Angle Mapper (SAM) algorithm with 73.68% accuracy. NDVI based approach is adopted to determine crop coefficients, and Hargreaves & Samani equation to compute reference crop evapotranspiration to assess CWR. In the study area 784.80 hectares of land is under arecanut crop cultivation with daily irrigation requirement estimated to be 28056.09 m3. From the analysis, the average CWR is observed to vary from 19 to 23 litres/day for juvenile to adult arecanut crop. Another interesting observation is that though 16.9% of the existing crop area is stressed due to crown choke disease, the water consumption of it is equivalent to that of healthy crops. Immediate response to address this issue will lead to enhanced plant life and thereby yield.

Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle

A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen aerial photography missions were conducted over the summer of 2013 using an off-the-shelf UAV and three-band, wide-angle, red, green, and blue (RGB) digital camera sensor. Images were post-processed, mosaicked, and georeferenced so automated classification and mapping could be completed. An adaptive cosine estimator (ACE) and spectral angle mapper (SAM) algorithm were used to complete the algal identification. Digital analysis of optical imagery correctly identified filamentous algae and background coverage 90% and 92% of the time, and tau coefficients were 0.82 and 0.84 for ACE and SAM, respectively. Thereafter, algal cover was characterized for a one-kilometer channel segment during each of the 18 UAV flights. Percent cover ranged from <5% to >50%, and increased immediately after vernal freshet, peaked in midsummer, and declined in the fall. Results indicate that optical remote sensing with UAV holds promise for completing spatially precise, and multi-temporal measurements of algae or submerged aquatic vegetation in shallow rivers with low turbidity and good optical transmission.

Hyperspectral analysis of clay minerals

Abstract. A study was carried out by collecting soil samples from parts of Gwalior and Shivpuri district, Madhya Pradesh in order to assess the dominant clay mineral of these soils using hyperspectral data, as 0.4 to 2.5 μm spectral range provides abundant and unique information about many important earth-surface minerals. Understanding the spectral response along with the soil chemical properties can provide important clues for retrieval of mineralogical soil properties. The soil samples were collected based on stratified random sampling approach and dominant clay minerals were identified through XRD analysis. The absorption feature parameters like depth, width, area and asymmetry of the absorption peaks were derived from spectral profile of soil samples through DISPEC tool. The derived absorption feature parameters were used as inputs for modelling the dominant soil clay mineral present in the unknown samples using Random forest approach which resulted in kappa accuracy of 0.795. Besides, an attempt was made to classify the Hyperion data using Spectral Angle Mapper (SAM) algorithm with an overall accuracy of 68.43 %. Results showed that kaolinite was the dominant mineral present in the soils followed by montmorillonite in the study area.

Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data

This study used a reef-up approach to map coral reef benthos, substrates and bathymetry, with high spatial resolution hyperspectral image data. It investigated a physics-based inversion method for mapping coral reef benthos and substrates using readily available software: Hydrolight and ENVI. Compact Airborne Spectrographic Imager (CASI) data were acquired over Heron Reef in July 2002. The spectral reflectance of coral reef benthos and substrate types were measured in-situ, and using the HydroLight 4.2 radiative transfer model a spectral reflectance library of subsurface reflectance was simulated using water column depths from 0.5–10.0 m at 0.5 m intervals. Using the Spectral Angle Mapper algorithm, sediment, benthic micro-algae, algal turf, crustose coralline algae, macro-algae, and live coral were mapped with an overall accuracy of 65% to a depth of around 8.0 m; in waters deeper than 8.0 m the match between the classified image and field validation data was poor. Qualitative validation of the maps showed accurate mapping of areas dominated by sediment, benthic micro-algae, algal turf, live coral, and macro-algae. A bathymetric map was produced for water column depths 0.5–10.0 m, at 0.5 m intervals, and showed high correspondence with in-situ sonar data (R2 value of 0.93).