scholarly journals Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

2018 ◽  
Vol 10 (8) ◽  
pp. 1208 ◽  
Author(s):  
Javier Marcello ◽  
Francisco Eugenio ◽  
Javier Martín ◽  
Ferran Marqués
Keyword(s):  
High Resolution ◽  
Aquatic Vegetation ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Shallow Waters ◽  
Benthic Habitats ◽  
Spectral Angle Mapper ◽  
Seabed Mapping

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

scholarly journals Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

2014 ◽  
Vol XL-8 ◽  
pp. 667-670 ◽  
Author(s):  
R. Vidhya ◽  
D. Vijayasekaran ◽  
M. Ahamed Farook ◽  
S. Jai ◽  
M. Rohini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Svm Classification ◽  
Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

scholarly journals Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

2019 ◽  
Vol 11 (8) ◽  
pp. 953 ◽  
Author(s):  
Tarin Paz-Kagan ◽  
Micha Silver ◽  
Natalya Panov ◽  
Arnon Karnieli
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Temperature Pattern ◽  
Multispectral Images ◽  
Invasive Plant Species ◽  
Distribution Maps

Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread.

scholarly journals Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 12 (3) ◽  
pp. 408
Author(s):  
Małgorzata Krówczyńska ◽  
Edwin Raczko ◽  
Natalia Staniszewska ◽  
Ewa Wilk
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Aerial Photographs ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Asbestos Cement ◽  
Feature Fitting

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats.

scholarly journals Mediterranean forest species mapping using hyperspectral imagery

2013 ◽  
Author(s):  
Γεωργία Γαλιδάκη
Keyword(s):  
Support Vector Machines ◽  
Hyperspectral Imagery ◽  
Mediterranean Forest ◽  
Support Vector ◽  
Forest Species ◽  
Spectral Angle Mapper ◽  
Vector Machines ◽  
Species Mapping ◽  
Spectral Angle

Τα Μεσογειακά δάση, παρότι είναι ένα μικρό κλάσμα της παγκόσμιας δασικής κάλυψης, χρειάζεται να γίνουν κατανοητά και να παρακολουθούνται ώστε να είναι δυνατή η διατήρησή τους. Χωρική πληροφορία, σχετικά με τη θέση τους, την έκτασή τους, τη δομή τους και τη βιοποικιλότητά τους, μεταξύ άλλων, είναι απαραίτητη και παραδοσιακά συγκεντρώνεται με κάθε διαθέσιμο τρόπο. Συγκεκριμένα, η χαρτογράφηση δασικών ειδών είναι απαιτούμενο και προϋπόθεση για ένα εύρος εφαρμογών στους τομείς της οικολογίας, βιολογίας, δασοκομίας και γεωργίας, όπως απογραφές, εκτίμηση βιοποικιλότητας, εκτίμηση κινδύνου πυρκαϊάς, σχεδιασμός πολιτικών προστασίας, διαχείριση φυσικών κινδύνων, παρακολούθηση αλλαγών και εκτίμηση δέσμευσης άνθρακα. Επιπρόσθετα, πληροφορίες για τα δασικά είδη είναι απαραίτητες για τις εθνικές υποχρεώσεις υποβολής εκθέσεων βάσει εθνικών και διεθνών πολιτικών, όπως η Σύμβαση Πλαίσιο των Ηνωμένων Εθνών για την Κλιματική Αλλαγή (UNFCCC) και το Πρωτόκολλο του Κιότο, η Σύμβαση του ΟΗΕ για τη Βιοποικιλότητα (UNCBD), το Συνεργατικό Πρόγραμμα για τη Μείωση των Εκπομπών από την Αποδάσωση και την Υποβάθμιση των Δασών στις Αναπτυσσόμενες χώρες του ΟΗΕ (UN-REDD), η Υπουργική Διάσκεψη για την Προστασία των Δασών στην Ευρώπη (MCPFE) και ο Εξορθολογισμός των ευρωπαϊκών δεικτών βιοποικιλότητας 2010 (SEBI2010). Η πρόοδος της υπερφασματικής τεχνολογίας παρέχει στους ερευνητές την ευκαιρία να διερευνήσουν προβλήματα που ήταν είτε δύσκολο είτε αδύνατο να προσεγγίσουν με χρήση πολυφασματικών δεδομένων, μεταξύ των οποίων και η χαρτογράφηση δασικών ειδών. Η παρούσα μελέτη εξετάζει τη χαρτογράφηση Μεσογειακών δασικών ειδών με βάση δορυφορική υπερφασματική εικόνα EO-1 Hyperion (30μ, 196δίαυλοι). Αξιολογήθηκαν δυο μεθοδολογίες ανάλυσης σε επίπεδο εικονοστοιχείου, συγκεκριμένα με βάση το Χαρτογράφο Φασματικής Γωνίας (Spectral Angle Mapper - SAM) και τις Μηχανές Διανυσμάτων Υποστήριξης (Support Vector Machines - SVM), όπως επίσης και μία μεθοδολογία αντικειμενοστρεφούς ανάλυσης (GEOBIA). Αυτές εφαρμόστηκαν σε δύο περιοχές μελέτης με διαφορετική σύνθεση και χωρικό μοτίβο ειδών, τη νήσο Θάσο και τον Ταξιάρχη Χαλκιδικής. Εκτενής εργασία πεδίου παρείχε τα δεδομένα αναφοράς για την εκτίμηση ακρίβειας των χαρτών. Το στάδιο της προεπεξεργασίας περιελάμβανε βήματα διορθώσεων και μείωση υπερφασματικής διάστασης. Στην περίπτωση της Θάσου, όπου υπήρχαν δύο είδη πεύκης, οι μεθοδολογίες με SAM, SVM και GEOBIA, πέτυχαν ολική ακρίβεια 94%, 89% και 85,3% αντίστοιχα. Στην περίπτωση του Ταξιάρχη, όπου υπήρχαν περισσότερα είδη, οι αντίστοιχες ολικές ακρίβειες που επιτεύχθηκαν ήταν 80%, 82,6% και 74,1%. Και οι τρεις μεθοδολογίες που αναπτύχθηκαν πέτυχαν πολύ ακριβή αποτελέσματα, σε μερικές περιπτώσεις εφάμιλλα χαρτών δασικής απογραφής. Η πρώτη (SAM) ήταν η πιο απλή στην εφαρμογή ενώ για τη δεύτερη χρειάστηκαν τόσο μια σειρά από παραμετροποιήσεις όσο και προσαρμοσμένο λογισμικό. Οι χαμηλότερες ακρίβειες της τελευταίας μεθοδολογίας (GEOBIA) μπορούν να αποδοθούν στον τρόπο εκτίμησής της, αφού στην περίπτωση της αντικειμενοστρεφούς ανάλυσης ερευνώνται ακόμα εναλλακτικές μέθοδοι εκτίμησης ακρίβειας, καλύτερα προσαρμοσμένες στη φύση του χώρου των αντικειμένων. Το αποτέλεσμα των προτεινόμενων μεθοδολογιών είναι δυνατό να καλύψουν τις τρέχουσες ανάγκες για γεωγραφικά δεδομένα βλάστησης, τόσο σε περιφερειακή όσο και σε εθνική κλίμακα. Επίσης, καταδεικνύουν την αξία των δορυφορικών υπερφασματικών εικόνων στη χαρτογράφηση δασικών ειδών της Μεσογείου.

scholarly journals Urban land cover classification using hyperspectral data

2014 ◽  
Vol XL-8 ◽  
pp. 751-754 ◽  
Author(s):  
G. Hegde ◽  
J. Mohammed Ahamed ◽  
R. Hebbar ◽  
U. Raj
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Urban Land ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Atmospheric Effects ◽  
Spectral Angle Mapper ◽  
Urban Land Cover ◽  
Lower Accuracy

Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers <i>viz.</i>, Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.

scholarly journals A HYPERSPECTRAL BASED METHOD TO DETECT CANNABIS PLANTATION IN INACCESSIBLE AREAS

2018 ◽  
Vol XLII-3 ◽  
pp. 547-551
Author(s):  
M. Houmi ◽  
B. Mohamadi ◽  
T. Balz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Spectral Signature ◽  
Spectral Angle Mapper ◽  
Planting Methods ◽  
Maximum Angle ◽  
Hyperion Data ◽  
Remote Sensing Techniques ◽  
Two Stages

The increase in drug use worldwide has led to sophisticated illegal planting methods. Most countries depend on helicopters, and local knowledge to identify such illegal plantations. However, remote sensing techniques can provide special advantages for monitoring the extent of illegal drug production. This paper sought to assess the ability of the Satellite remote sensing to detect Cannabis plantations. This was achieved in two stages: 1- Preprocessing of Hyperspectral data EO-1, and testing the capability to collect the spectral signature of Cannabis in different sites of the study area (Morocco) from well-known Cannabis plantation fields. 2- Applying the method of Spectral Angle Mapper (SAM) based on a specific angle threshold on Hyperion data EO-1 in well-known Cannabis plantation sites, and other sites with negative Cannabis plantation in another study area (Algeria), to avoid any false Cannabis detection using these spectra. This study emphasizes the benefits of using hyperspectral remote sensing data as an effective detection tool for illegal Cannabis plantation in inaccessible areas based on SAM classification method with a maximum angle (radians) less than 0.03.

scholarly journals UAV-BASED HYPERSPECTRAL DATA ANALYSIS FOR URBAN AREA MAPPING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 621-626
Author(s):  
N. A. Suran ◽  
H. Z. M. Shafri ◽  
N. S. N. Shaharum ◽  
N. A. W. M. Radzali ◽  
V. Kumar
Keyword(s):  
Urban Area ◽  
Low Cost ◽  
Hyperspectral Data ◽  
Pollution Monitoring ◽  
Support Vector ◽  
Land Resource ◽  
Easy Method ◽  
Spectral Angle Mapper ◽  
Aerial Vehicle ◽  
Artificial Neural Network Ann

Abstract. A recent development in low-cost technology such as Unmanned Aerial Vehicle (UAV) offers an easy method for collecting geospatial data. UAV plays an important role in land resource surveying, urban planning, environmental protection, pollution monitoring, disaster monitoring and other applications. It is a highly adaptable technology that is continuously changing in innovative ways to provide greater utility. Thus, this study aimed to evaluate the capability of UAV-based hyperspectral data for urban area mapping. In order to do the mapping, Artificial Neural Network (ANN), Support Vector Machine (SVM), Maximum Likelihood (ML) and Spectral Angle Mapper (SAM) were used to classify the urban area. The classifications involved seven classes: concrete, aluminium, flexible pavement, clay tile, interlocking block, tree and grass. Then, the overall accuracies obtained from ANN, SVM, ML and SAM for 0.3 m spatial resolution images were 92.33%, 85.86%, 83.41% and 46.55% with the kappa coefficient of 0.91, 0.83, 0.80 and 0.38 respectively. Thus, the classification results showed that the powerful and intelligent ANN algorithm produced the highest accuracy compared to the other three algorithms. Overall, mapping of urban area using UAV-based hyperspectral data and advanced algorithms could be the way forward in producing updated urban area maps.

scholarly journals AVIRIS-NG Data for Geological Applications in Southeastern Parts of Aravalli Fold Belt, Rajasthan

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 16
Author(s):  
Sameeksha Mishra ◽  
Shovan L. Chattoraj ◽  
Alen Benny ◽  
Richa U. Sharma ◽  
P. K. Champati Ray
Keyword(s):  
High Resolution ◽  
Near Infrared ◽  
Country Rock ◽  
Satellite Image ◽  
Infrared Region ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Fold Belt ◽  
Spectral Angle Mapper ◽  
Iron Bearing

Advanced techniques using high resolution hyperspectral remote sensing data has recently evolved as an emerging tool with potential to aid mineral exploration. In this study, pertinently, five mosaicked scenes of Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data of southeastern parts of the Aravalli Fold belt in Jahazpur area, Rajasthan, were processed. The exposed Proterozoic rocks in this area is of immense economic and scientific interest because of richness of poly-metallic mineral resources and their unique metallogenesis. Analysis of high resolution multispectral satellite image reveals that there are many prominent lineaments which acted as potential conduits of hydrothermal fluid emanation, some of which resulted in altering the country rock. This study takes cues from studying those altered minerals to enrich our knowledge base on mineralized zones. In this imaging spectroscopic study we have identified different hydrothermally altered minerals consisting of hydroxyl, carbonate and iron-bearing species. Spectral signatures (image based) of minerals such as Kaosmec, Talc, Kaolinite, Dolomite, and Montmorillonite were derived in SWIR (Short wave infrared) region while Iron bearing minerals such as Goethite and Limonite were identified in the VNIR (Visible and Near Infrared) region of electromagnetic spectrum. Validation of the target minerals was done by subsequent ground truthing and X-ray diffractogram (XRD) analysis. The altered end members were further mapped by Spectral Angle Mapper (SAM) and Adaptive Coherence Estimator (ACE) techniques to detect target minerals. Accuracy assessment was reported to be 86.82% and 77.75% for SAM and ACE respectively. This study confirms that the AVIRIS-NG hyperspectral data provides better solution for identification of endmember minerals.

Sign in / Sign up

Export Citation Format

Share Document