scholarly journals AVALIAÇÃO DA QUALIDADE DE CLASSIFICADORES DE IMAGENS LANDSAT 8 EM AMBIENTE COMPUTACIONAL SAGA GIS PARA MAPEAMENTO DE COBERTURA DA TERRA NO BIOMA CERRADO

2021 ◽  
Vol 7 (20) ◽  
pp. 202128
Author(s):  
Antonia Sueli Silva Sousa ◽  
Paulo Roberto Mendes Pereira ◽  
Audivan Ribeiro Garcês Júnior
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Landsat 8 ◽  
Kappa Index ◽  
El Sistema ◽  
Computer Environment ◽  
Processing Accuracy ◽  
Attribute Extraction ◽  
Global Accuracy ◽  
Object Of Study

QUALITY ASSESSMENT OF LANDSAT 8 IMAGE CLASSIFIERS IN A SAGA GIS COMPUTER ENVIRONMENT FOR LAND COVERING MAPPING IN THE CERRADO BIOMEEVALUACIÓN DE LA CALIDAD DE LOS CLASIFICADORES DE IMAGEN LANDSAT 8 EN UN ENTORNO COMPUTACIONAL SAGA GIS PARA EL MAPEO DE COBERTURA DE TIERRAS EN EL BIOMA DE CERRADORESUMOUma das principais aplicações das imagens de satélites é a caracterização da cobertura terrestre, que a partir do uso de técnicas de classificação permite monitorar as transformações espaciais da superfície terrestre. O Sistema Automatizado de Análise Geociêntífica – Saga Gis apresenta um conjunto de ferramentas voltado à análise geográfica, incluindo pacotes de classificação de imagens digitais, onde se destacam os classificadores: Maxver, Mahalanobis, distância mínima, paralelepípedo. O objetivo deste artigo é avaliar o potencial dos classificadores de imagens do Saga Gis no bioma Cerrado, sendo objeto de estudo, o município de Brejo-MA. Foi utilizada uma imagem Landsat 8 de 2017, com resolução espacial de 30 metros. A metodologia consistiu na aplicação de um conjunto de técnicas de tratamento digital de imagens, segmentação, extração de atributos e classificação. A análise dos dados pautou-se na comparação visual e análise da exatidão global e de índice Kappa. O classificador Maxver apresentou os melhores resultados para o Kappa e exatidão global, já os piores valores foram associados ao classificador paralelepípedo.Palavras-chave: Geotecnologia; Processamento de Imagem; Acurácia, Mapeamento. ABSTRACTOne of the main applications of satellite images is the characterization of terrestrial coverage, which from the use of classification techniques allows to monitor the spatial transformations of the terrestrial surface. The System for Automated Geoscientific Analyzes-Saga Gis presents a set of tools aimed at geographic analysis, including digital image classification packages, in which the classifiers stand out: Maxver, Mahalanobis, minimum distance, parallelepiped. The objective of this article is to evaluate the potential of the Saga Gis image classifiers in the Cerrado biome, being the object of study, the municipality of Brejo-MA. It was to use a Landsat 8 image (2017), with a spatial resolution of 30 meters. The methodology consisted of applying a set of techniques for digital image processing, segmentation, attribute extraction and classification. Data analysis was based on visual comparison and analysis of global accuracy and Kappa index. The Maxver classifier presented the best results for Kappa and overall accuracy, whereas the worst values were associated with the parallelepiped classifier.Keywords: Geotechnology; Image Processing; Accuracy; Mapping.RESUMENUna de las principales aplicaciones de las imágenes de satélite es la caracterización de la cobertura terrestre, que, a partir del uso de técnicas de clasificación, permite el seguimiento de las transformaciones espaciales de la superficie terrestre. El Sistema de Análisis Geocientífico Automatizado (Saga Gis) presenta un conjunto de herramientas orientadas al análisis geográfico, que incluyen paquetes de clasificación de imágenes digitales, en los que destacan los clasificadores: Maxver, Mahalanobis, distancia mínima, paralelepípedo. El objetivo de este artículo es evaluar el potencial de los clasificadores de imágenes Saga Gis en el bioma del Cerrado, siendo objeto de estudio, el municipio de Brejo-MA. Se utilizó una imagen Landsat 8 de 2017 con una resolución espacial de 30 metros. La metodología consistió en aplicar un conjunto de técnicas de procesamiento, segmentación, extracción de atributos y clasificación de imágenes digitales. El análisis de los datos se basó en la comparación visual y el análisis de la precisión global y el índice Kappa. El clasificador Maxver presentó los mejores resultados para Kappa y precisión general, mientras que los peores valores se asociaron con el clasificador paralelepípedo.Palabras clave: Geotecnología; Procesamiento de imágenes; Precisión; Mapeo.

scholarly journals REMOTE SENSING IMAGE TIME SERIES METRICS FOR DISTINCTION BETWEEN PASTURE AND CROPLANDS USING THE RANDOM FOREST CLASSIFIER

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 557-562
Author(s):  
M. A. A. Rodrigues ◽  
H. N. Bendini ◽  
A. R. Soares ◽  
T. S. Körting ◽  
L. M. G. Fonseca
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Random Forest ◽  
Landsat 8 ◽  
Kappa Index ◽  
Object Based ◽  
One Year ◽  
Global Accuracy ◽  
Rural Production ◽  
Potential Use

Abstract. Pasture and croplands play an important role in Brazil’s economic and political scenarios, once its PIB (Raw Internal Product) is mainly based on what is exported from the rural production, such as meat and soybean, and government, with its regulations, is part-responsible for the establishment and maintaining of the conditions so that the trades can go well. In addition, these two types of land use correspond together to aprox. one third of the country extension. Moreover, frequently lands occupation is subject of discussion concerning its potential use for the reason of conflicts including Brazilian traditional communities, landless people and big farmers. Considering it, mapping pasture and croplands accurately is crucial for the country administration, in both economic and political spheres. Certainly, remote sensing is the very manner to tackle this issue, although this may not be an easy task due to the spectral similarity between these patterns. This work, hence, aims to distinct pasture from croplands in an experimental subset area of Brazilian Cerrado biome, using remote sensing metric images derived from one-year time series of the Landsat 8 products. In order to achieve this goal, we utilized six bands of the OLI sensor and calculated seven metrics, attaining a compiled dataset with 42 layers. We performed an object-based supervised classification with the Random Forest algorithm, considering both spectral and geometrical attributes. Results showed global accuracy of 80%, with Kappa index of 0.6, and the potential time series have in separating targets spectrally similar.

scholarly journals Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

2020 ◽  
Vol 85 ◽  
pp. 101983 ◽  
Author(s):  
Jonathan da Rocha Miranda ◽  
Marcelo de Carvalho Alves ◽  
Edson Ampélio Pozza ◽  
Helon Santos Neto
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Satellite Imagery ◽  
Digital Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Coffee Berry

scholarly journals ANÁLISE COMPARATIVA DE MÉTODOS DE CLASSIFICAÇÃO SUPERVISIONADA APLICADA AO MAPEAMENTO DA COBERTURA DO SOLO NO MUNICÍPIO DE MEDICILÂNDIA, PARÁ

2018 ◽  
Vol 4 (13) ◽  
pp. 26
Author(s):  
Jones Remo Barbosa Vale ◽  
Jamer Andrade da Costa ◽  
Jefferson Ferreira dos Santos ◽  
Elton Luis Silva da Silva ◽  
Artur Trindade Favacho
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Minimum Distance ◽  
Supervised Classification ◽  
Satellite Images ◽  
Soil Cover ◽  
Landsat 8 ◽  
Kappa Index ◽  
Global Accuracy

COMPARATIVE ANALYSIS THE METHODS OF SUPERVISED CLASSIFICATION APPLIED TO THE MAPPING OF SOIL COVER IN THE MUNICIPALITY OF MEDICILÂNDIA, PARÁANÁLISIS COMPARATIVO DE MÉTODOS DE CLASIFICACIÓN SUPERVISADA APLICADA AL MAPEO DE LA COBERTURA DEL SUELO EN EL MUNICIPIO DE MEDICILÂNDIA, PARÁAs imagens de satélite são produtos gerados por sensoriamento remoto e, estão associadas aos fenômenos e eventos que ocorrem na superfície a partir do registro e da análise das interações entre a radiação eletromagnética e os alvos. O objetivo do trabalho é comparar métodos de classificação supervisionada de imagens de satélite para o mapeamento da cobertura do solo. A área de estudo compreende o município de Medicilândia, localizado no sudoeste paraense. Para a realização do trabalho foram utilizados imagens do satélite Landsat 8, sensor OLI-TIRS, cenas 226/062 e 227/062. Foram realizados os testes de classificação, utilizando três classificadores: Distância Mínima, Distância Mahalanobis e Máxima Verossimilhança. Na etapa de classificação foram identificadas as seguintes classes: água, nuvem, sombra de nuvem, solo exposto, vegetação primária e vegetação secundária. Para fins de avaliação da fidedignidade da classificação de cada método foram calculados, o Índice Kappa e a Exatidão Global. A classificação pelo método Máxima Verossimilhança obteve maior exatidão apresentando Índice Kappa de 0,920 e Exatidão Global 96% quando comparada à classificação pelos métodos Distância Mínima e Distância Mahalanobis, que apresentaram Índice Kappa de 0,842 e 0,845 e Exatidão Global 92% respectivamente. As técnicas de classificação supervisionada são ferramentas essenciais no processo de mapeamento da cobertura do solo de grandes áreas, visto que dispondo-se de recursos limitados, imagens de baixo custo e de sistemas livres para processamento e integração das informações, é possível obter parâmetros com altos níveis de precisão, sendo fundamentais para subsidiar o planejamento territorial e ambiental.Palavras-chave: Sensoriamento Remoto; Classificação de Imagens de Satélite; Cobertura do Solo.ABSTRACTThe satellite images are products generated by remote sensing and are associated with phenomena and events that occur on the surface from the recording and analysis of interactions between electromagnetic radiation and targets. The objective of this work is to compare methods of supervised classification of satellite images for the mapping of the soil cover. The study area comprises the municipality of Medicilândia, located in southwest of Para. In order to perform the work, were used images from the Landsat 8 satellite, OLI-TIRS sensor, scenes 226/062 and 227/062. The classification tests were performed using three classifiers: Minimum Distance, Mahalanobis Distance and Maximum Likelihood. In the classification processe were identified the following classes: water, cloud, cloud shadow, exposed soil, primary vegetation and secondary vegetation. For the purposes of evaluating the reliability of the classification of each method were calculated, Kappa Index and Global Accuracy. The classification by the Maximum Likelihood method obtained a greater accuracy presenting Kappa Index of 0,920 and Global Accuracy 96% when compared to the classification by the Minimum Distance and Mahalanobis Distance, which presented Kappa Index of 0,842 and 0,845 and Global Accuracy 92% respectively. The supervised classification techniques are essential tools in the mapping process of large-area soil cover, since with limited resources, low-cost images and free systems for processing and integrating information, it is possible to obtain parameters with high levels of precision, being fundamental to subsidize territorial and environmental planning.Keywords: Remote Sensing; Classification of Satellite Images; Soil Cover.RESUMENLas imágenes de satélite son productos generados por la detección remota y están asociados a los fenómenos y eventos que ocurren en la superficie a partir del registro y del análisis de las interacciones entre la radiación electromagnética y los blancos. El objetivo del trabajo es comparar métodos de clasificación supervisada de imágenes de satélite para el mapeo de la cobertura del suelo. El área de estudio comprende el municipio de Medicilândia, ubicado en el suroeste paraense. Para la realización del trabajo se utilizaron imágenes del satélite Landsat 8, sensor OLI-TIRS, escenas 226/062 y 227/062. Se utilizaron tres clasificadores: Distancia Mínima, Distancia Mahalanobis y Máxima Verosimilitud. En la etapa de clasificación se identificaron las siguientes clases: agua, nube, sombra de nube, suelo expuesto, vegetación primaria y vegetación secundaria. Para evaluar la confianza de la clasificación de cada método se ha calculado, el Índice Kappa y la Exactitud Global. La clasificación por Máxima Verosimilitud obtuvo mayor exactitud presentando Índice Kappa de 0,920 y Exactitud Global 96% cuando comparada a la clasificación por Distancia Mínima y Distancia Mahalanobis, que presentaron Índice Kappa de 0,842 y 0,845 y Exactitud Global 92% respectivamente. Las técnicas de clasificación supervisada son herramientas esenciales en el proceso de mapeo de la cobertura del suelo de grandes áreas, ya que disponiendo de recursos limitados, imágenes de bajo costo y de sistemas libres para procesamiento e integración de la información, es posible obtener parámetros con altos niveles de precisión, siendo fundamentales para subsidiar la planificación territorial y ambiental.Palabras clave: Sensoramiento Remoto; Clasificación de Imágenes de Satélite; Cobertura del Suelo.

Numerical Simulation of the Deep Mining of Phosphate Ore Body Based on Digital Image Processing

2012 ◽  
Vol 616-618 ◽  
pp. 521-527
Author(s):  
Jun Wei Yao ◽  
Sheng Zhou Li ◽  
Ming Hui Li
Keyword(s):  
Numerical Simulation ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Underground Mining ◽  
Grid Cell ◽  
Open Pit ◽  
Triangular Grid ◽  
Uniform Mesh ◽  
Object Of Study

Based on digital image processing, use Matlab mathematical software to write Flac3D modeling interface program, and suppress noise transported from CAD software, for establishing numerical simulation model rapidly. With the No.59 exploration line profile picture of Yunnan Phosphate Group Jinning mine as the object of study, which is transformed from the open pit to deep underground mining, to establish Flac3D model mined with the way of room and pillar method study, it shows that: (1) using the method of multi-threshold segmentation, build the appropriate mask to suppress image noise effectively; (2) with controlling the horizontal grid cell size, achieve uniform mesh of the model; mesh at the strata border with the triangular grid, for weakening the jagged edges; (3) as the stopes excavated gradually, the movement extent of roof and floor is increasing influenced by mining; the roof is relatively stable, while the stress concentration factor of room pillars is bigger, therefore some measures should be taken to prevent the mine wall caving. The numerical simulation results provide a reference for the design feasibility study of phosphate mining and have a guiding significance for parameter optimization as well.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

Complementary Z-Contrast Imaging and Digital Image Processing

1985 ◽  
Vol 43 ◽  
pp. 304-305
Author(s):  
K. N. Colonna ◽  
G. Oliphant
Keyword(s):  
Image Processing ◽  
Heavy Metal ◽  
Digital Image Processing ◽  
Digital Image ◽  
Biological Tissue ◽  
Biological Imaging ◽  
Tissue Preparation ◽  
Contrast Imaging ◽  
Imaging Tool ◽  
Z Contrast

Harmonious use of Z-contrast imaging and digital image processing as an analytical imaging tool was developed and demonstrated in studying the elemental constitution of human and maturing rabbit spermatozoa. Due to its analog origin (Fig. 1), the Z-contrast image offers information unique to the science of biological imaging. Despite the information and distinct advantages it offers, the potential of Z-contrast imaging is extremely limited without the application of techniques of digital image processing. For the first time in biological imaging, this study demonstrates the tremendous potential involved in the complementary use of Z-contrast imaging and digital image processing.Imaging in the Z-contrast mode is powerful for three distinct reasons, the first of which involves tissue preparation. It affords biologists the opportunity to visualize biological tissue without the use of heavy metal fixatives and stains. For years biologists have used heavy metal components to compensate for the limited electron scattering properties of biological tissue.

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