A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

Renza, Diego; Martínez, Elvira; Arquero, Agueda

doi:10.1109/lgrs.2012.2193372

Cited by 55 publications

(26 citation statements)

References 19 publications

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“…Several methods, including principle component analysis (PCA), change vector analysis (CVA), support vector machine (SVM), multivariate alteration detection (MAD), have proven to be effective in various applications [12][13][14][15][16][17][18]. The CVA is a binary change detection method that identifies the changes using the magnitude between two spectral vectors.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Byun¹,

Han

Chae

2015

Remote Sensing

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Abstract:Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In this paper, a novel unsupervised change detection approach based on image fusion is introduced. The approach aims to extract the reliable flood extent from very high-resolution (VHR) bi-temporal images. The method takes an advantage of the spectral distortion that occurs during image fusion process to detect the change areas by flood. To this end, a change candidate image is extracted from the fused image generated with bi-temporal images by considering a local spectral distortion. This can be done by employing a universal image quality index (UIQI), which is a measure for local evaluation of spectral distortion. The decision threshold for the determination of changed pixels is set by applying a probability mixture model to the change candidate image based on expectation maximization (EM) algorithm. We used bi-temporal KOMPSAT-2 satellite images to detect the flooded area in the city of N′djamena in Chad. The performance of the proposed method was visually and quantitatively compared with existing change detection methods. The results showed that the proposed method achieved an overall accuracy (OA = 75.04) close OPEN ACCESSRemote Sens. 2015, 7 10348 to that of the support vector machine (SVM)-based supervised change detection method. Moreover, the proposed method showed a better performance in differentiating the flooded area and the permanent water body compared to the existing change detection methods.

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Section: Introductionmentioning

confidence: 99%

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Byun¹,

Han

Chae

2015

Remote Sensing

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“…In another context, the comparison is made by means of data reduction techniques that emphasize on the data with the highest variance, such as in the case of PCA-based (Principal Component Analysis) methods; in this case, the comparison method needs to be complemented with a framework to obtain change labels [1,2,3]. Finally, change detection magnitude and direction can be approached by analyzing feature vectors in the method known as Change Vector Analysis (CVA), which gives an intensity image and a direction image of change from the length and direction of the difference vector [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos¹,

Renza²,

Ballesteros³

2018

DYNA

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Unsupervised change detection (UCD) is a subject of Remote Sensing whose objective is to detect the differences between two multi-temporal images. In some cases, spectral similarity indices have been used as the comparison block in algorithms of UCD. The aim of this paper is to show in a quantitative way the performance of four spectral similarity indices in the correct identification of changes. Comparison is performed in terms of precision (overall accuracy and kappa index) over medium and high-resolution images (SPOT-5: Satellite Pour l'Observation de la Terre and Quickbird), with a reference obtained through a post-classification method (based on Support Vector Machines, SVM). The results show dependence on the automatic thresholding technique, as well as on the classes associated with the change.Keywords: change detection; spectral indices; remote sensing; accuracy assessment.Evaluación de índices de similitud espectral en esquemas de detección de cambios no supervisados Resumen La detección de cambios de forma no-supervisada (UCD) es un área de teledetección, cuyo objetivo consiste en encontrar las diferencias entre dos imágenes multi-temporales. En algunos casos, los índices de similitud espectral son utilizados como bloque de comparación de UCD. El objetivo de este documento consiste en analizar de forma cuantitativa el desempeño de cuatro índices de similitud espectral en la correcta identificación de cambios. La evaluación se realiza en términos de la precisión (mediante la precisión global e índice kappa) utilizando imágenes de media y alta resolución (SPOT-5: Satélite Para la Observación de la Tierra y Quickbird), así como una imagen de cambio de referencia obtenida a través de un método de post-clasificación (basado en Máquinas de Soporte Vectorial, SVM). Los resultados obtenidos presentan dependencia con la técnica automática de umbralización, así como con las clases asociadas con el cambio.Palabras clave: detección de cambios; índices espectrales; teledetección; evaluación de precisión.

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“…In recent literature, various algorithms for automatic change detection have been proposed, such as change vector analysis (CVA) [10], Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) [15], universal image quality index (UIQI) [16], and multivariate alteration detection (MAD) [17]. These algorithms have proven to be effective in many applications.…”

Section: Introductionmentioning

confidence: 99%

“…The ERGAS method shows a relative dimensionless global error in the synthesis for local change detection in high-resolution multi-temporal images. This method exploits the benefits of the ERGAS index to maintain the independence of the number of spectral bands, and it does not need any parameters or assumptions to obtain the difference image [15]. The UIQI method is easy to calculate, can handle several types of image noise, and relates to the change information.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Wang

Choi

et al. 2017

Remote Sensing

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Abstract:Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method-a measure for the spectral distortion of change information-is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

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A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

Cited by 55 publications

References 19 publications

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Evaluation of spectral similarity indices in unsupervised change detection approaches

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

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