scholarly journals A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

2021 ◽  
Vol 13 (24) ◽  
pp. 5164
Author(s):  
Eduardo R. Oliveira ◽  
Leonardo Disperati ◽  
Fátima L. Alves
Keyword(s):  
Remote Sensing ◽  
Selection Procedure ◽  
Unsupervised Classification ◽  
Multi Index ◽  
Image Differencing ◽  
Burned Areas ◽  
Index Approach ◽  
Central Portugal ◽  
Multispectral Remote Sensing

This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000–2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

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.

A graph-based active learning method for classification of remote sensing images

2018 ◽  
Vol 16 (04) ◽  
pp. 1850023
Author(s):  
Lian-Zhi Huo ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
Active Learning ◽  
Image Classification ◽  
Selection Procedure ◽  
Experimental Tests ◽  
Maximization Problem ◽  
Support Vector ◽  
Training Samples

Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity selection procedure is converted to the densest k-subgraph [Formula: see text] maximization problem in graph theory. The [Formula: see text] maximization problem is solved by a greedy algorithm. The proposed technique is compared with competing methods adopted in RS community. Experimental tests are performed on very high resolution (VHR) multispectral and hyperspectral images. Experimental results demonstrate that the proposed technique leads to comparable or even better classification accuracies with respect to competing methods on the two datasets.

scholarly journals BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 83-88 ◽  
Author(s):  
M. Papadomanolaki ◽  
M. Vakalopoulou ◽  
S. Zagoruyko ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
High Level ◽  
Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the <i>AlexNet</i>, <i>AlexNet-small</i> and <i>VGG</i> models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates <i>i.e.</i>, above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

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.

Sign in / Sign up

Export Citation Format

Share Document