Métodos de segmentación de nubes en imágenes satelitales

Vargas, Ernesto Gómez; Neira, Nelson Obregón; Arango, Diego Fernando Rocha

doi:10.14483/udistrital.jour.tecnura.2013.2.a08

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Para la segmentación del fondo de la imagen, se propone el método de inspección de picos, basado en las crestas del histograma de la imagen [22]. La primera cresta se obtiene por inspección, analizando el primer pico del histograma.…”

Section: Transformación a Las Imágenes De Entradaunclassified

Comparativa entre la técnica de umbralización binaria y el método de Otsu para la detección de personas

et al. 2021

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En procesos de detección por imágenes en las que existe variación de luminosidad entre pixeles, se requieren técnicas que permitan obtener valores óptimos y adaptables de umbral ante dichas variaciones. Por ello, se realiza una comparativa entre la técnica de umbralización binaria y el método adaptativo de Otsu, en videos con fondo dinámico y estático, ponderando el tiempo de respuesta del algoritmo, memoria utilizada, requerimiento de la unidad central de procesos y aciertos en las detecciones, en los lenguajes de Python y M (Matlab). Las técnicas en Python presentan mejores resultados en cuanto a tiempo de respuesta y espacio de memoria; mientras que, al utilizar Matlab, se presenta el menor porcentaje de requerimiento de máquina. Asimismo, el método de Otsu mejora el porcentaje de aciertos en 12.89 % y 11.3 % para videos con fondo dinámico y estático, respecto a la técnica de umbralización binaria.

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Section: Transformación a Las Imágenes De Entradaunclassified

Comparativa entre la técnica de umbralización binaria y el método de Otsu para la detección de personas

et al. 2021

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“…Currently there is no doubt there is a large amount of images available of the Earth surface; and digital image processing techniques and the algorithms to carry out pixel classification have also become accessible (Camacho Velasco, Vargas García, & Arguello Fuentes, 2016;Neira & Rocha, 2013). Efforts have been made around the supervised and unsupervised approaches based on support vector machines (SVM) (Lizarazo, 2008) and random forests (Tso & Mather, 2009) and have concluded that these two are the most promising techniques.…”

Section: Introductionmentioning

confidence: 99%

Comparison of maximum likelihood, support vector machines, and random forest techniques in satellite images classification

Medina

Atehortúa²

2019

Tecnura

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Context: Nowadays, the images of the Earth surface and the algorithms for their classification are widely available. In particular, the algorithms are promising in the differentiating of cotton crops stages, but it is necessary to establish the capabilities of the different algorithms in order to identify their advantages, and disadvantages. Method: This paper describes the assessment process in which the Support Vector Machines (SVM) and random-forest technique (decision trees) are compared with the maximum likelihood estimation when differentiating the stages of cotton crops. A RapidEye satellite image of a geographic area in the municipality of San Pelayo, Cordoba (Colombia), is used for the study. Using a set of sampling polygons, a random sample of 6000 pixels was taken (2000 training and 4000 for validating the classifications.) Confusion matrices, and R (data processing and analysis software) were used during the validation process Results: The maximun likelihood estimation presented a correct classification percentage of 68.95%. SVM correctly classified 81.325% of the cases and the decision trees correctly classified 78.925%. The confidence test for the classifications showed non-overlapping intervals, and SVM obtained the highest values. Conclusions: It was possible to confirm the superiority of the technique based on support vector machines for the proposed verification zones. However, this technique requires a number of classes that comprehensively represent the variations of the image (in order to guarantee a minimum number of support vectors) to avoid confusion in the classification of non-sampled areas. This was less evident in the other two classification techniques analysed.

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