scholarly journals Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Pilot Study ◽  
Data Processing ◽  
High Latitude ◽  
Hyperspectral Images ◽  
The Arctic ◽  
Wetland Mapping ◽  
Kappa Index ◽  
Spectral Angle Mapper ◽  
Spectral Angle

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

scholarly journals Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

2008 ◽  
Vol 8 (7) ◽  
pp. 1310-1316 ◽  
Author(s):  
P. Beatriz Garcia-Allende ◽  
Olga M. Conde ◽  
JesÚs Mirapeix ◽  
Ana M. Cubillas ◽  
JosÉ M. Lopez-Higuera
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Processing Method ◽  
Spectral Angle Mapper ◽  
Data Processing Method ◽  
Spectroscopic Sensors ◽  
Spectral Angle

scholarly journals PERBANDINGAN ALGORITMA PADA METODE KLASIFIKASI SUPERVISED BERBASIS CITRA ALOS AVNIR-2 UNTUK PEMETAAN MANGROVE

2020 ◽  
Vol 6 (1) ◽  
pp. 1-9
Author(s):  
CHRISTIAN S. IMBURI
Keyword(s):  
Maximum Likelihood ◽  
Mahalanobis Distance ◽  
Minimum Distance ◽  
Supervised Classification ◽  
Near Infrared ◽  
Classification Method ◽  
Image Process ◽  
Infrared Band ◽  
Spectral Angle Mapper ◽  
Spectral Angle

The goal of this research was to provide an appropriate algorithm for mapping the mangrove area in Anday. Supervised classification method applied that consisted of several algorithmic alternatives such as parallel piped, minimum distance algorithm, mahalanobis distance, maximum likelihood, and spectral angle mapper. In addition, this study was conducted by applying the image process of a near-infrared band of ALOS AVNIR-2 and then analysis was carried out to leverage the accuracy of the range of spectral values through field survey and to test the accuracy of mangrove zonation maps which was based the supervised classification method. The results revealed that the overall accuracy of parallelepiped was 29%,  and 41.18% for minimum distance algorithm, mahalanobis distance was 58.82%, the maximum likelihood was 50%, and spectral angle mapper was 58.82%.

scholarly journals LAND COVER CLASSIFICATION PERFORMANCE OF MULTISPECTRAL RTK UAVs

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 489-492
Author(s):  
U. G. Sefercik ◽  
T. Kavzoglu ◽  
I. Colkesen ◽  
S. Adali ◽  
S. Dinc ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Land Cover ◽  
Spatial Data ◽  
Minimum Distance ◽  
Low Cost ◽  
Land Cover Classification ◽  
Spectral Angle Mapper ◽  
Classification Techniques ◽  
Object Based ◽  
Spectral Angle

Abstract. Unmanned air vehicle (UAV) became an alternative airborne remote sensing technique, due to providing very high resolution and low cost spatial data and short processing time. Particularly, optical UAVs are frequently utilized in various applications such as mapping, agriculture, and forestry. Especially for precise agriculture purposes, the UAVs were equipped with multispectral cameras which enables to classify land cover easily. In this study, the land cover classification potential of DJI Phantom IV Multispectral, one of the most preferred agricultural UAVs in the world, was investigated using spectral angle mapper, minimum distance and maximum likelihood pixel-based classification techniques and object-based classification. In the investigation, a part of Gebze Technical University (GTU) Northern Campus, includes a large variety of land cover classes, was selected as the study area. The UAV aerial photos were achieved from 70 m flight altitude and processed using structure from motion (SfM)-based image matching software Agisoft Metashape. The pixel-based and object-based land cover classification processes were completed with ENVI and eCognition software respectively. 16 independent land cover classes were classified and the results demonstrated that the accuracies are 73.46% in spectral angle mapper, 75.27% in minimum distance and 93.56% in maximum likelihood pixel-based classification techniques and 90.09% in nearest neighbour object-based classification.

scholarly journals AVALIAÇÃO DA ACURÁCIA DE ALGORITMOS DE CLASSIFICAÇÃO DE IMAGENS ORBITAIS NA BAÍA DA BABITONGA, NORDESTE DE SANTA CATARINA

2021 ◽  
Vol 14 (6) ◽  
pp. 3577
Author(s):  
Celso Voos Vieira ◽  
Pedro Apolonid Viana
Keyword(s):  
Feature Extraction ◽  
Maximum Likelihood ◽  
Mahalanobis Distance ◽  
Minimum Distance ◽  
Spectral Information ◽  
Classification Algorithms ◽  
Neural Net ◽  
Spectral Angle Mapper ◽  
Information Divergence ◽  
Spectral Angle

O objetivo deste trabalho foi a avaliação da acurácia de algoritmos de classificação do uso e cobertura do solo, quando aplicados a uma imagem orbital de média resolução espacial. Para esse estudo foram utilizadas as bandas espectrais da faixa do visível e infravermelho próximo, do sensor Operational Land Imager – OLI na Baía da Babitonga/SC. Foram propostas nove classes de cobertura do solo, que serviram como controle para testar 11 algoritmos classificadores: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper e Spectral Information Divergence. O classificador Maximum Likelihood foi o que apresentou o melhor desempenho, obtendo um índice Kappa de 0,89 e acurácia global de 95,5%, sendo capaz de distinguir as nove classes de cobertura do solo propostas. Evaluation of the Accuracy of Orbital Image Classification Algorithms in Babitonga Bay, northeast of Santa Catarina A B S T R A C TThe objective of this work was to evaluate the classification algorithms accuracy of the soil use and cover when applied to a spatial mean orbital image. For this study we used the visible and near infrared spectral bands of the Operational Land Imager - OLI sensor in Babitonga Bay / SC. Nine classes of soil cover were proposed, which served as control to test 11 classifier algorithms: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper and Spectral Information Divergence. The Maximum Likelihood classifier presented the best performance, obtaining a Kappa index of 0.89 and a global accuracy of 95.5%, being able to distinguish the nine proposed classes of soil cover.Keywords: Algorithms Accuracy, Babitonga Bay, Orbital image, Remote sensing, Soil Use and Cover. 

scholarly journals Paleocene-Eocene volcanic segmentation of the Norwegian-Greenland seaway reorganized high-latitude ocean circulation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Jussi Hovikoski ◽  
Michael B. W. Fyhn ◽  
Henrik Nøhr-Hansen ◽  
John R. Hopper ◽  
Steven Andrews ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
High Latitude ◽  
Seismic Reflection ◽  
Arctic Region ◽  
Climatic Conditions ◽  
The Arctic ◽  
Early Eocene ◽  
Subaerial Exposure ◽  
Thermal Maximum ◽  
Volcanic Facies

AbstractThe paleoenvironmental and paleogeographic development of the Norwegian–Greenland seaway remains poorly understood, despite its importance for the oceanographic and climatic conditions of the Paleocene–Eocene greenhouse world. Here we present analyses of the sedimentological and paleontological characteristics of Paleocene–Eocene deposits (between 63 and 47 million years old) in northeast Greenland, and investigate key unconformities and volcanic facies observed through seismic reflection imaging in offshore basins. We identify Paleocene–Eocene uplift that culminated in widespread regression, volcanism, and subaerial exposure during the Ypresian. We reconstruct the paleogeography of the northeast Atlantic–Arctic region and propose that this uplift led to fragmentation of the Norwegian–Greenland seaway during this period. We suggest that the seaway became severely restricted between about 56 and 53 million years ago, effectively isolating the Arctic from the Atlantic ocean during the Paleocene–Eocene thermal maximum and the early Eocene.

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