scholarly journals Unsupervised classification of lava flows in Harrat Lunayyir using remote sensing and GIS

2019 ◽  
Vol 12 (16) ◽  
Author(s):  
Azizah Al Shehri ◽  
Agust Gudmundsson
Keyword(s):  
Remote Sensing ◽  
Unsupervised Classification ◽  
Remote Sensing And Gis ◽  
Lava Flows ◽  
Harrat Lunayyir

scholarly journals Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

2020 ◽  
Vol 12 (3) ◽  
pp. 759
Author(s):  
Jūratė Sužiedelytė Visockienė ◽  
Eglė Tumelienė ◽  
Vida Maliene
Keyword(s):  
Remote Sensing ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Economic Losses ◽  
Classification Methods ◽  
Earth Remote Sensing ◽  
Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

Low-altitude remote sensing and GIS-based analysis of cropmarks: classification of past thermal-contraction-crack polygons in central western Poland

Geomorphology ◽  
2017 ◽  
Vol 293 ◽  
pp. 418-432 ◽  
Author(s):  
Marek W. Ewertowski ◽  
Andrzej Kijowski ◽  
Izabela Szuman ◽  
Aleksandra M. Tomczyk ◽  
Leszek Kasprzak
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Thermal Contraction ◽  
Low Altitude

scholarly journals SOIL CLASSIFICATION OF BAHARIYA OASIS USING REMOTE SENSING AND GIS TECHNIQUES

2013 ◽  
Vol 4 (9) ◽  
pp. 921-947
Author(s):  
A. A. Elnaggar ◽  
Kh. H. El-Hamdi ◽  
A. B. A. Belal ◽  
M. M. El-Kafrawy
Keyword(s):  
Remote Sensing ◽  
Soil Classification ◽  
Remote Sensing And Gis ◽  
Gis Techniques ◽  
Bahariya Oasis

scholarly journals CHANGE DETECTION SOFTWARE USING SELF-ORGANIZING FEATURE MAPS

2012 ◽  
Vol 30 (4) ◽  
pp. 505
Author(s):  
Nilton Correia da Silva ◽  
Osmar Abílio de Carvalho Júnior ◽  
Antonio Nuno de Castro Santa Rosa ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
High Dimensional Data ◽  
Unsupervised Classification ◽  
Fine Tuning ◽  
Primary Data ◽  
High Dimensional ◽  
Feature Maps ◽  
Self Organizing

Os mapas auto-organizáveis (SOFM) consistem em um tipo de rede neural artificial que permite a conversão de dados de alta dimensão, complexos e não lineares, em simples relações geométricas com baixa dimensionalidade. Este método também pode ser utilizado para a classificação de imagens de sensoriamento remoto, pois permite a compressão de dados de alta dimensão preservando as relações topológicas dos dados primários. Este trabalho objetiva desenvolver uma metodologia eficaz para a utilização de mapas auto-organizáveis na detecção de mudanças. No presente estudo o SOFM é utilizado para a classificação não supervisionada de dados de sensoriamento remoto, considerando os seguintes atributos: espaciais (x, y), espectrais e temporais. O método é empregado na região oeste da Bahia, que teve recentemente um aumento significativo em monoculturas. Testes foram realizados com os parâmetros do SOFM com o objetivo de refinar o mapa de detecção demudanças. O SOFM possibilita uma melhor seleção de células e dos correspondentes vetores de peso, que mostram o processo de ordenação e agrupamento hierárquicodos dados. Esta informação é essencial para identificar mudanças ao longo do tempo. Um programa em linguagem C ++ do método proposto foi desenvolvido. ABSTRACT. Self-organizing feature maps (SOFM) consist of a type of artificial neural network that allows the conversion from high-dimensional data into simple geometric relationships with low-dimensionality. This method can also be used for classification of remote sensing images because it allows the compression of high dimensional data while preserving the most important topological and metric relationships of the primary data. This paper aims to develop an effective methodology forusing self-organizing maps in change detection. In this study, SOFM is used for unsupervised classification of remote sensing data, considering the following attributes: spatial (x and y), spectral and temporal. The method is tested and simulated in the western region of Bahia that has observed a significant increase in mechanized agriculture. Tests were performed with the SOFM parameters for the purpose of fine tuning a change detection map. The SOFM provides the best selection of cell and corresponding adjustment of weight vectors, which show the process of ordering and hierarchical clustering of the data. This information is essential to identify changes over time. All algorithms were implemented in C++ language.Keywords: unsupervised classification; land cover; multitemporal analysis; remote sensing

scholarly journals Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

2019 ◽  
Vol 11 (23) ◽  
pp. 2800
Author(s):  
Alon Dadon ◽  
Moshe Mandelmilch ◽  
Eyal Ben-Dor ◽  
Efrat Sheffer
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Hyperspectral Remote Sensing ◽  
Mediterranean Forest ◽  
Full Potential ◽  
Sequential Image ◽  
Forest Plants ◽  
Intensive Sampling

In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.

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