Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment

Leichtle, Tobias; Geiß, Christian; Wurm, Michael; Lakes, Tobia; Taubenböck, Hannes

doi:10.1016/j.jag.2016.08.010

Cited by 120 publications

(88 citation statements)

References 48 publications

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“…Research on change detection techniques have looked for new procedures to optimize some of the following characteristics related to the change analysis and its types: area and rate, spatial distribution, trajectories of land cover types, and accuracy evaluation of the results [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…The aim of these methods is to generate a change detection map, where modified and transition classes in land use can be identified. In this case, the changes are detected and labeled through supervised classification schemes; therefore, post-classification comparison is a suitable kind of method to implement when sufficient training sample data are available [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…NDVI [8], NDWI [9], NDDI [9], etc.). 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%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of spectral similarity indices in unsupervised change detection approaches

Ramos¹,

Renza²,

Ballesteros³

2018

DYNA

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“…The most general change detection framework in remote sensing comprises of feature extraction and decision making. At the feature level, some methods focus on the completeness of color, texture and structural information [8,9], while others intend to use object-based analysis and MRF to emphasis spatial information [10][11][12]. At the decision level, most of these methods can be categorized into supervised, semi-supervised and unsupervised methods [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Considering reference samples are often not available in real applications, the proposed approach is designed in an unsupervised way. To detect changes in the increasing amount of available HRRS imagery, Leichtle et al [11] proposed an object-based approach using principal component analysis (PCA) and k-means clustering for the discrimination of changed and unchanged buildings. Byun et al [29] introduced a novel unsupervised change detection approach based on image fusion.…”

Section: Introductionmentioning

confidence: 99%

Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields

Yang

Hua

et al. 2017

Remote Sensing

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Abstract:Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data labelling tasks are needed, and the sample annotation process with real categories is tedious and costly. To relieve the burden of labelling and to obtain satisfactory results, we propose an interactive change detection framework based on active learning and Markov random field (MRF). More specifically, a limited number of representative objects are found in an unsupervised way at the beginning. Then, the very limited samples are labelled as "change" or "no change" to train a simple binary classification model, i.e., a Gaussian process model. By using this model, we then select and label the most informative samples by "the easiest" sample selection strategy to update the former weak classification model until the detection results do not change notably. Finally, the maximum a posteriori (MAP) change detection is efficiently computed via the min-cut-based integer optimization algorithm. The time consuming and laborious manual labelling process can be reduced substantially, and a desirable detection result can be obtained. The experiments on several WorldView-2 images demonstrate the effectiveness of the proposed method.

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Image Segmentation for Remote Sensing Analysis

2022

Image Segmentation

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Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment

Cited by 120 publications

References 48 publications

Evaluation of spectral similarity indices in unsupervised change detection approaches

Evaluation of spectral similarity indices in unsupervised change detection approaches

Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields

Image Segmentation for Remote Sensing Analysis

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