Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Molina, Íñigo; Martínez, Elvira; Arquero, Agueda; Pajares, Gonzalo; Sánchez, José

doi:10.3390/s120303528

Cited by 17 publications

(17 citation statements)

References 45 publications

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“…Generally, at a first stage a Change Detection Index (CDI) is generated, and then by means of thresholding, a Change Detection Map (or binary image) is derived. With respect to this thresholding process, it can be accomplished automatically, as it has already been performed in previous studies [10] based in the algorithms described by [11]. These strategies have also been applied in part of this research.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Molina

Martínez

Morillo

et al. 2016

Sensors

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In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal “invariant features” is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a “change map”, which can be accomplished by means of the CDI’s informational content. For this purpose, information metrics such as the Shannon Entropy and “Specific Information” have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf’s) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances.

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Section: Introductionmentioning

confidence: 99%

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Molina

Martínez

Morillo

et al. 2016

Sensors

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“…En la práctica, a menudo, se realizan estudios comparativos entre diferentes enfoques con el objetivo de encontrar los mejores resultados para una aplicación específica. En cualquier caso, los métodos no supervisados se muestran más atractivos desde el punto de vista operativo, especialmente para el análisis de grandes conjuntos de datos.Con el fin de aprovechar al máximo las cualidades de las diferentes propuestas de generación de los mapas de cambios, algunos autores [13], [14], sugieren su uso simultáneo y una fusión posterior de las diferentes fuentes de información sobre la presencia o ausencia de cambios en las escenas.En este trabajo, se propone una metodología semisupervisada de DC mediante la aplicación de métodos no supervisados basados en la diferencia, cociente y cálculo de la distancia de , [16], [17], en imágenes multitemporales SPOT pancromáticas de alta resolución espacial. Una vez obtenidas las imágenes, resultado de la aplicación de estos Indices de Cambios, se unen para generar una imagen multibanda, a la que posteriormente se aplica un clasificador supervisado basado en Máquinas de Soporte…”

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“…Con el fin de aprovechar al máximo las cualidades de las diferentes propuestas de generación de los mapas de cambios, algunos autores [13], [14], sugieren su uso simultáneo y una fusión posterior de las diferentes fuentes de información sobre la presencia o ausencia de cambios en las escenas.…”

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Optimizing Panchromatic Image Change Detection Based on Change Index Multiband Image Analysis

Izquierdo

Hidalgo

Sánchez

2015

IEEE Latin Am. Trans.

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Abstract-This work proposes an optimization of a semisupervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem. I. INTRODUCCIONS E denomina Detección de Cambios (DC) a un proceso mediante el cual se identifican las diferencias en el estado de un objeto o fenómeno al observarlo en diferentes momentos, es decir, se trata de cuantificar efectos temporales utilizando información multitemporal [1]. La determinación de áreas de cambio en imágenes de la misma escena tomada en diferentes momentos, presenta un notable interés, debido a que ofrece un gran número de aplicaciones en diversas disciplinas [2], incluyendo entre otras: la video-vigilancia [3], el diagnóstico y tratamiento médico, el apoyo a la conducción de vehículos y la detección remota.Concretamente, la Detección de Cambios en imágenes registradas mediante sensores remotos, está considerada como una rama de la tecnología que constituye la Teledetección. Lo que contribuye a este hecho, es la resolución temporal de los sensores de satélites, cuya inherente repetitividad en su órbita, permite que se puedan registrar con cierta regularidad, imágenes de un determinado área objetivo. Además la continua mejora de la tecnología espacial ha propiciado el desarrollo de sensores de alta resolución, que proveen de [6]. En el primer grupo se incluyen métodos que requieren información de referencia del terreno, necesaria para obtener mapas temáticos de transiciones entre clases de coberturas terrestres que cambian; se basan en el uso de clasificadores supervisados [7]. Esta información de referencia se puede obtener mediante campañas de campo, por fotointerpretación o desde el conocimiento previo de la escena. En estos métodos, como inconvenientes se pueden indicar: el coste humano y temporal que supone la obtención de la información de referencia y el posible error cometido en el proceso de clasificación. Por el contrario, los métodos no supervisados no necesitan datos de referencia para la generación del mapa bin...

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“…The experimental results certified the effectiveness of both proposed methods. Molina et al [22] suggested a multisource CD approach for multi resolution data sets. After extraction of various change indices, different thresholding algorithms were applied to these indices.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

2015

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Detection of damages caused by natural disasters is a delicate and difficult task due to the time constraints imposed by emergency situations. Therefore, an automatic Change Detection (CD) algorithm, with less user interaction, is always very interesting and helpful. So far, there is no existing CD approach that is optimal and applicable in the case of (a) labeled samples not existing in the study area; (b) multi-temporal images being corrupted by either noise or non-normalized radiometric differences; (c) difference images having overlapped change and no-change classes that are non-linearly separable from each other. Also, a low degree of automation is not optimal for real-time CD applications and also one-dimensional representations of classical CD methods hide the useful information in multi-temporal images. In order to resolve these problems, two automatic kernel-based CD algorithms (KCD) were proposed based on kernel clustering and support vector data description (SVDD) algorithms in high dimensional Hilbert space. In this paper (a) a new similarity space was proposed in order to increase the separation between change and no-change classes, and also to decrease the processing time, (b) three kernel-based approaches were proposed for transferring the multi-temporal images from spectral space into high dimensional Hilbert space, (c) automatic approach was proposed to extract the precise labeled samples; (d) kernel parameter w a s selected automatically by optimizing an improved cost function and (e) initial value of the kernel parameter was estimated by a statistical method based on the L2-norm distance. Two different datasets including OPEN ACCESSRemote Sens. 2015, 7 12830Quickbird and Landsat TM/ETM+ imageries were used for the accuracy of analysis of proposed methods. The comparative analysis showed the accuracy improvements of kernel clustering based CD and SVDD based CD methods with respect to the conventional CD techniques such as Minimum Noise Fraction, Independent Component Analysis, Spectral Angle Mapper, Simple Image differencing and Image Rationing, and also the computational cost analysis showed that implementation of the proposed CD method in similarity space decreases the processing runtime.

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Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Cited by 17 publications

References 45 publications

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Optimizing Panchromatic Image Change Detection Based on Change Index Multiband Image Analysis

A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

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