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 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 Cartografía del aguacate en el sur del estado de México mediante tratamiento digital de imágenes sentinel-2

2020 ◽  
Vol 11 (4) ◽  
pp. 865-879
Author(s):  
Dulce Karen Figueroa-Figueroa ◽  
Jose Francisco Ramírez Dávila ◽  
Xanat Antonio-Némiga ◽  
Andrés González Huerta
Keyword(s):  
Persea Americana ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Information Divergence ◽  
Spectral Angle ◽  
Sentinel 2

El cultivo de aguacate (Persea americana Mill.) es uno de los más importantes en México, entre los estados con mayor producción se encuentra el Estado de México, que es el tercer estado productor a nivel nacional. Coatepec Harinas y Donato Guerra son dos de los municipios más representativos en lo respectivo a esta actividad; sin embargo, no existe un censo que especifique la superficie del cultivo, por lo que el objetivo de esta investigación fue probar métodos de índices de vegetación, algoritmos spectral angle mapper (SAM) y spectral information divergence (SID) y la combinación de estos en las imágenes del sensor Sentinel-2 para evaluar su desempeño en la identificación de áreas plantadas con el cultivo de aguacate. Los resultados se validaron con una matriz de confusión y la comparación de los datos de referencia de entrenamiento y validación. El algoritmo SID alcanzó una precisión de 97.5% para detectar aguacate, mientras que el tratamiento SAM obtuvo una precisión de 63.1%. La combinación de SID con el índice Anthocyanin Reflectance Index 1 (ARI1), proporcionó un mejor resultado sobre la cartografía de validación regional con 85% de precisión. Otras combinaciones de índices y tratamientos dieron resultados inferiores al 50% de la precisión por lo que no se recomiendan. Esta metodología podría ser probada para la detección de otros cultivos de interés comercial, dado que Sentinel-2 muestra ser una alternativa viable para este tipo de estudios, teniendo una buena resolución espectral, además de ser de fácil acceso y manipulación.

scholarly journals Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

2020 ◽  
Vol 12 (13) ◽  
pp. 2154 ◽  
Author(s):  
Ke Wang ◽  
Ligang Cheng ◽  
Bin Yong
Keyword(s):  
Hyperspectral Image ◽  
Similarity Measures ◽  
Kernel Functions ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Spectral Similarity ◽  
Information Divergence ◽  
Rbf Kernel ◽  
Hyperspectral Classification ◽  
Spectral Angle

Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

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 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.

PERBANDINGAN ALGORITMA KLASIFIKASI MULTISPEKTRAL UNTUK IDENTIFIKASI OBJEK MANGROVE MENGGUNAKAN FOTO UDARA DIGITAL FORMAT KECIL

2021 ◽  
pp. 795
Author(s):  
Syafiq Muhammad Ridha ◽  
Rozky Rahmat ◽  
Muhammad Ari Purnomo ◽  
Muhammad Kamal ◽  
Nurul Khakhim
Keyword(s):  
Maximum Likelihood ◽  
Mahalanobis Distance ◽  
Random Sampling ◽  
Minimum Distance ◽  
Confusion Matrix ◽  
Stratified Random Sampling ◽  
Digital Format

Hutan mangrove di Indonesia semakin hari semakin menyempit luasannya sehingga diperlukan inventarisasi dan monitoring keberadaan mangrove. Identifikasi objek mangrove dapat dilakukan dengan menggunakan bantuan Foto Udara Digital Format Kecil (FUDFK) yang merekam objek melalui wahana drone dengan kamera saku yang dimodifikasi. Penelitian ini bertujuan untuk (1) menerapkan algoritma klasifikasi terselia dan tidak terselia untuk pemetaan mangrove di Baros, Kabupaten Bantul, (2) dan menentukan algoritma klasifikasi multispektral dengan nilai uji akurasi tertinggi pada identifikasi mangrove. FUDFK yang digunakan adalah hasil pemotretan menggunakan kamera Canon powershoot yang memiliki band biru, hijau, dan NIR. Algoritma klasifikasi yang diperbandingkan adalah Maximum Likelihood, Parallelepiped, Mahalanobis Distance, dan Minimum Distance untuk klasifikasi terselia, serta IsoData dan K-Means untuk klasifikasi tak terselia. Hasil klasifikasi kemudian diuji akurasinya menggunakan confusion matrix berdasarkan pengambilan data di lapangan pada 40 sampel objek secara stratified random sampling. Hasil penelitian berupa visualisasi hasil identifikasi vegetasi mangrove menggunakan beberapa algoritma klasifikasi terpilih, algoritma klasifikasi terbaik, serta nilai uji akurasi (confusion matrix). Visualisasi hasil identifikasi vegetasi mangrove dilakukan pada beberapa algoritma terpilih menunjukkan bahwa pada algoritma terselia maximum likelihood memiliki nilai akurasi tertinggi yaitu 67,5 % dengan indeks kappa 0,5517 serta algoritma tidak terselia K-Means dengan nilai akurasi terendah yaitu 27,5% dengan indeks kappa 0,3898.

scholarly journals Experiment of Multispectral Images using Spectral Angle Mapper Algorithm for Land Cover Classification

2019 ◽  
Vol 8 (6S4) ◽  
pp. 96-99
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Multispectral Images ◽  
Rapid Urbanization ◽  
Spectral Angle Mapper ◽  
Time Period ◽  
Steep Decline ◽  
Spectral Angle

Urbanization plays a key role in the health of the water bodies in any region. In a rapidly growing country like India, especially Bangalore district, rapid urbanization has seen a steep decline in the number of water bodies the region is famous for. In this paper, Land Use and Land Cover change is analysed for the remotely sensed images of Bangalore District using Spectral Angle Mapper Algorithm. Data for the purpose of analysis was obtained from BHUVAN (NRSC, ISRO). The study area is Bangalore District and data was collected from the time period 2008-2016. The major classes used in the classification are Land(Built-up), water bodies (Lakes), Vegetation (Gardens), Soil (Barren and fertile). The satellite images and the accompanying classification algorithms indicate that the percentage of water bodies have drastically shrunk (from 2.9% in 2008to1.8% in 2016) in the area of study. The results of this study can be used by the civic authorities to implement decisions to conserve the water bodies in the area.

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