MIMOSA: An Automatic Change Detection Method for SAR Time Series

Quin, Guillaume; Pinel‐Puysségur, Béatrice; Nicolas, Jean‐Marie; Loreaux, P

doi:10.1109/tgrs.2013.2288271

Cited by 55 publications

(35 citation statements)

References 27 publications

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“…Many studies exist that define thresholds based on experience or trial-and-error procedures to separate a one-dimensional feature space derived from bi-temporal image pairs [10,11]. Several threshold approximation methods have been proposed in recent literature to overcome the subjective bias and poor transferability of manual thresholding [12,13]. Liu and Yamazaki (2011) [14] analyze the change feature histograms to pick a threshold value.…”

Section: Introductionmentioning

confidence: 99%

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Wieland

Yamazaki

2016

Remote Sensing

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This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single-and multi-temporal X-and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however, indicate that given dynamic parameter tuning, feature selection and an appropriate sampling approach, already small training samples (100 samples per class) are sufficient to produce high change detection rates. Moreover, the experiments show a good generalization ability of SVM which allows transfer and reuse of trained learning machines.

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Section: Introductionmentioning

confidence: 99%

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Wieland

Yamazaki

2016

Remote Sensing

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“…The proposed change criteria approximate likelihood ratio test R ALRT and generalized likelihood ratio test R GLRT are compared with some state-of-the-art methods, such as Log-Ratio operator (Rignot and van Zyl, 1993), the generalized likelihood ratio test (GLRT) proposed in (Lombardo and Oliver, 2001), Wilcoxon Test based change criterion (Krylov et al, 2012) and Method for generalIzed Means Ordered Series Analysis (Quin et al, 2013), summarized in Table 2.…”

Section: Change Detection Methodsmentioning

confidence: 99%

“…When dealing with a multi-temporal data set (more than 2 dates), the analysis among them is much more complex. As said in the introduction, we will focus in this work on binary changes which means that we will not take into (c) The RGB composition between a and b by MIMOSA (Quin et al, 2013).…”

Section: Change Classificationmentioning

confidence: 99%

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NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering

Deledalle

Tupin

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tThis paper presents a likelihood ratio test based method of change detection and classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on chAnge criterion MAtrix (NORCAMA). This method involves three steps: (1) multi-temporal pre-denoising step over the whole image series to reduce the effect of the speckle noise; (2) likelihood ratio test based change criteria between two images using both the original noisy images and the denoised images; (3) change classification by a normalized cut based clustering-and-recognizing method on change criterion matrix (CCM). The experiments on both synthetic and real SAR image series show the effective performance of the proposed framework. Ó

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“…For instance, different techniques based on the ratio of pixel intensities have been proposed [2,7,20,53,60,63]. Other relevant methods are based on neural networks [4,49] or on the joint distribution of the pixel intensities [15,29,52].…”

Section: Dirichlet Process ψ(·)mentioning

confidence: 99%

A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images

Prendes¹,

Chabert²,

Pascal³

et al. 2016

SIAM J. Imaging Sci.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17269 (2015), pp. 799-812] to deal with images captured by heterogeneous sensors. The main idea of the suggested strategy was to model the objects contained in an analysis window by mixtures of distributions. The manifold defined by these mixtures was then learned using training data belonging to unchanged areas. The changes were finally detected by thresholding an appropriate distance to the estimated manifold. This paper goes a step further by introducing a Bayesian nonparametric framework allowing us to deal with an unknown number of objects in analysis windows without specifying an upper bound for this number. A Markov random field is also introduced to account for the spatial correlation between neighboring pixels. The proposed change detector is validated using different sets of synthetic and real images (including pairs of optical images and pairs of optical and radar images) showing a significant improvement when compared to existing algorithms.

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MIMOSA: An Automatic Change Detection Method for SAR Time Series

Cited by 55 publications

References 27 publications

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering

A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images

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