Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos, Jeisson Fabian; Renza, Diego; Ballesteros, Miguel

doi:10.15446/dyna.v85n204.68355

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Así mismo, Ieno et al [13] realizan una comparativa entre los valores de umbral obtenidos por cálculos matemáticos simples con única iteración de imagen, y el método de Otsu, logrando diferencias en términos de consumo de recursos y frecuencia máxima de operación. Por su parte, Ramos et al proponen una evaluación en Matlab de índices de similitud espectral en ambientes no supervisados, obteniendo imágenes con intensidades variables que se comparan con una imagen de referencia originada posterior a la clasificación [14].…”

Section: Estado Del Arteunclassified

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: Estado Del Arteunclassified

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

et al. 2021

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“…The computational cost of kernel-based methods is higher than the cost of direct comparison using similarity metrics. Therefore, a spectral similarity index allows easy application and interpretation of DIs [27].…”

Section: Generating Label Datamentioning

confidence: 99%

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Song

Kim

2020

Remote Sensing

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Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

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“…In this paper, the spectral similarity was based on SAM obtained by considering each 5 spectrum as a vector in the abundance maps of endmembers. This algorithm can be calculated as Equation (1) [14][15][16].…”

Section: Stepmentioning

confidence: 99%

Probability Estimation of Change Maps Using Spectral Similarity

Jafarzadeh

Hasanlou

2019

3rd International Electronic Conference on Remote Sensing

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Change Detection (CD), which is a process of identifying changes that have occurred in a geographical area over the time, plays a key role in many applications, including assessing natural disasters, monitoring crops, and managing water resources. In the past decades, many CD techniques have been proposed. Hence, evaluating and analyzing of the probability of changes and interpreting them is an essential task which leads to better management of natural resources and the prevention of disasters. For this purpose, we adopted an approach to estimate the probability of changes that have occurred in the image scene. Based on this approach, changed pixels are categorized and labeled as probabilities (in percentage format). In this paper, the proposed framework consists of the following four steps. Firstly, this research produces a final binary change map (BCM) through combining the results of some of popular binary CD methods that have been proposed in the literature. Then an unmixing process is adopted and in the next step the spectral similarity of pixels is calculated in the abundance maps of endmembers. A measurement of spectral similarity identifies the finer spectral differences between the two hyperspectral images (HSIs). Finally, spectral similarity values are masked by the final BCM resulting in a probability map of changes. The experimental results verified that the method is able to obtain good results and may be well suited for hyperspectral CD applications.

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Evaluation of spectral similarity indices in unsupervised change detection approaches

Cited by 4 publications

References 20 publications

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

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

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Probability Estimation of Change Maps Using Spectral Similarity

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