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Improving SAR-Based Urban Change Detection by Combining MAP-MRF Classifier and Nonlocal Means Similarity Weights
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.ORCID iD: 0000-0002-1135-4192
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.
2014 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 7, no 10, 4288-4300 p.Article in journal (Refereed) Published
Abstract [en]

In remote sensing change detection, Markov random field (MRF) has been used successfully to model the prior probability using class-labels dependencies. MRF has played an important role in the detection of complex urban changes using optical images. However, the preservation of details in urban change analysis turns out to be a highly complex task if multi-temporal SAR images with their speckle are to be used. Here, the ability of MRF to preserve geometric details and to combat speckle effect at the same time becomes questionable. Blob-region phenomenon and fine structures removal are common consequences of the application of traditional MRF-based change detection algorithm. To overcome these limitations, the iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step. This probability model, which characterizes the relationship between pixels' class-labels in a nonlocal scale, has the potential to preserve spatial details and to reduce speckle effects. Two multitemporal SAR datasets were used to assess the proposed algorithm. Experimental results using three density functions [i.e., the log normal (LN), generalized Gaussian (GG), and normal distributions (ND)] have demonstrated the efficiency of the proposed approach in terms of detail preservation and noise suppression. Compared with the traditional MRF algorithm, the proposed approach proved to be less-sensitive to the value of the contextual parameter and the chosen density function. The proposed approach has also shown less sensitivity to the quality of the initial change map when compared with the ICM algorithm.

Place, publisher, year, edition, pages
2014. Vol. 7, no 10, 4288-4300 p.
Keyword [en]
Change detection, Markov random field (MRF), multitemporal SAR images, Nonlocal means (NLM), speckle, urban
National Category
Physical Geography
URN: urn:nbn:se:kth:diva-159401DOI: 10.1109/JSTARS.2014.2347171ISI: 000346977200025ScopusID: 2-s2.0-84920195559OAI: diva2:784452
Swedish National Space Board

QC 20150129

Available from: 2015-01-29 Created: 2015-01-29 Last updated: 2015-05-29Bibliographically approved
In thesis
1. Urban Change Detection Using Multitemporal SAR Images
Open this publication in new window or tab >>Urban Change Detection Using Multitemporal SAR Images
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. This thesis investigates urban change detection using multitemporal SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate effective methods for reduction of the speckle effect in change detection, (3) to investigate spatio-contextual change detection, (4) to investigate object-based unsupervised change detection, and (5) to investigate a new technique for object-based change image generation. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR and ENVISAT ASAR sensors were used for pixel-based change detection. For the object-based approaches, TerraSAR-X images were used.

In Paper I, the unsupervised detection of urban change was investigated using the Kittler-Illingworth algorithm. A modified ratio operator that combines positive and negative changes was used to construct the change image. Four density function models were tested and compared. Among them, the log-normal and Nakagami ratio models achieved the best results. Despite the good performance of the algorithm, the obtained results suffer from the loss of fine geometric detail in general. This was a consequence of the use of local adaptive filters for speckle suppression. Paper II addresses this problem using the nonlocal means (NLM) denoising algorithm for speckle suppression and detail preservation. In this algorithm, denoising was achieved through a moving weighted average. The weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, principle component analysis (PCA) was used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the number of significant PCA components to be retained for weights computation and the required noise variance were proposed. The experimental results showed that the NLM algorithm successfully suppressed speckle effects, while preserving fine geometric detail in the scene. The analysis also indicates that filtering the change image instead of the individual SAR images was effective in terms of the quality of the results and the time needed to carry out the computation.

The Markov random field (MRF) change detection algorithm showed limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle. To overcome this problem, Paper III utilizes the NLM theory to define a nonlocal constraint on pixels class-labels. The iterated conditional mode (ICM) scheme for the optimization of the MRF criterion function is extended to include a new step that maximizes the nonlocal probability model. Compared with the traditional MRF algorithm, the experimental results showed that the proposed algorithm was superior in preserving fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map.

Paper IV investigates object-based unsupervised change detection using very high resolution TerraSAR-X images over urban areas. Three algorithms, i.e., Kittler-Illingworth, Otsu, and outlier detection, were tested and compared. The multitemporal images were segmented using multidate segmentation strategy. The analysis reveals that the three algorithms achieved similar accuracies. The achieved accuracies were very close to the maximum possible, given the modified ratio image as an input. This maximum, however, was not very high. This was attributed, partially, to the low capacity of the modified ratio image to accentuate the difference between changed and unchanged areas. Consequently, Paper V proposes a new object-based change image generation technique. The strong intensity variations associated with high resolution and speckle effects render object mean intensity unreliable feature. The modified ratio image is, therefore, less efficient in emphasizing the contrast between the classes. An alternative representation of the change data was proposed. To measure the intensity of change at the object in isolation of disturbances caused by strong intensity variations and speckle effects, two techniques based on the Fourier transform and the Wavelet transform of the change signal were developed. Qualitative and quantitative analyses of the result show that improved change detection accuracies can be obtained by classifying the proposed change variables. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xiii, 87 p.
, Trita-SOM, ISSN 1654-2754 ; 2015:07
Change detection, High resolution, Image denoising, Kittler-Illingworth, MAP-MRF, Multitemporal SAR images, Nonlocal means, Object-based, Otsu, Outlier detection, Remote sensing, SAR speckle, Urban
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
urn:nbn:se:kth:diva-168216 (URN)978-91-7595-612-1 (ISBN)
Public defence
2015-06-12, 4055, Drottning Kristinas väg 30, Stockholm, 10:00 (English)

QC 20150529

Available from: 2015-05-29 Created: 2015-05-28 Last updated: 2015-05-29Bibliographically approved

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