Osama Yousif

Multitemporal Spaceborne SAR Data for Urban Change Detection in China

The objective of this research is to examine effective methods for urban change detection using multitemporal spaceborne SAR data in two rapid expanding cities in China. One scene of ERS-2 SAR C-VV image was acquired in Beijing in 1998 and in shanghai in 1999 respectively and one scene of ENVISAT ASAR C-VV image was acquired in near-anniversary dates in 2008 in Beijing and Shanghai. To compare the SAR images from different dates, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no change classes. The results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of these methods were of 0.82 and 0.71 for Beijing and Shanghai respectively while the false alarm rates were 2.7% and 4.75%. The findings indicated that the change accuracies obtained using Kittler-Illingworth algorithm vary depending on how the assumed conditional class density function fits the histograms of change and no change classes.

Improving Urban Change Detection From Multitemporal SAR Images Using PCA-NLM

Multitemporal synthetic aperture radar (SAR) images have been increasingly used in change detection studies. However, the presence of speckle is the main disadvantage of this type of data. To reduce speckle, many local adaptive filters have been developed. Although these filters are effective in reducing speckle in homogeneous areas, their use is often accompanied with the degradation of spatial details and fine structures. In this paper, we investigate a nonlocal means (NLM) denoising algorithm that combines local structures with a global averaging scheme in the context of change detection using multitemporal SAR images. First, the ratio image is logarithmically scaled to convert the multiplicative noise model to an additive model. A multidimensional change image is then constructed using image neighborhood feature vectors. Principle component analysis is then used to reduce the dimensionality of the neighborhood feature vectors. Recursive linear regression combined with fitting-accuracy assessment strategy is developed to determine the number of significant PC components to be retained for similarity weight computation. An intuitive method to estimate the unknown noise variance (necessary to run the NLM algorithm) based on the discarded PC components is also proposed. The efficiency of the method has been assessed using two different bitemporal SAR datasets acquired in Beijing and Shanghai, respectively. For comparison purposes, the algorithm is also tested against some of the most commonly used local adaptive filters. Qualitative and quantitative analyses of the algorithm have demonstrated the efficiency of the algorithm in recovering the noise-free change image while preserving the complex structures in urban areas.

A novel approach for object-based change image generation using multitemporal high-resolution SAR images

ABSTRACT Object-based change detection offers a unique approach for high-resolution images to capture meaningful detailed change information while suppressing noise in change detection results. In this approach, mean intensities of objects are commonly used as a feature and images comparison is carried out based on simple mathematical operations such as ratioing. The strong intensity variations within an object – a consequence of high spatial resolution – combined with synthetic aperture radar (SAR) image speckle degrade the accuracy of object mean intensity estimate, and consequently, affect the quality of the estimated object-based change image. A change quantification approach that takes into account the characteristics of high-resolution SAR images, that is, SAR speckle and the strong intensity variation, is proposed. By descending to the pixel level, a new representation of change data (i.e. the change signal) is proposed. With this representation, change quantification boils down to measuring the roughness of the change signal. Two techniques to assess the intensity of change at the object-level, based on Fourier and wavelet transforms (WT) of the change signal, are proposed. Their main advantages lie in their ability to capture the dominant change behaviour of the object, while being insusceptible to irrelevant disturbances. The proposed approach is evaluated using two multitemporal data sets of TerraSAR-X images acquired over Beijing and Shanghai. The qualitative and quantitative analyses of the results demonstrate the superior discrimination power of the proposed change variables compared with the object-based modified ratio (MR) and the absolute log ratio (LR) images.

Change Detection Techniques: A Review

With its synoptic view and the repeatability, satellite remote sensing can provide timely, accurate and consistent information about earth’s surface for cost-effective monitoring of environmental changes. In this chapter, recent development in change detection techniques using multitemporal remotely sensed images were reviewed. The chapter covers change detection methods for both optical and SAR images. Various aspects of change detection processes were presented including data preprocessing, change image generation and change detection algorithms such as unsupervised and supervised change detection as well as pixel-based and object-based change detection. The review shows that significant progress has been made in the field of change detection and innovative methods have been developed for change detection using both multitemporal SAR and optical data. Attempts have been made for change detection using multitemporal multisensor/cross-sensor images. The review also identified a number of challenges and opportunities in change detection.

Unsupervised change detection using multitemporal spaceborne SAR data: A case study in Beijing

The objective of this research is to examine unsupervised change detection methods using multitemporal spaceborne SAR data for urbanization monitoring in Beijing. One scene of ENVISAT ASAR C-VV image was acquired in July, 2008 and one scene of ERS-2 SAR C-VV image was acquired in July, 1998. To compare the two SAR images, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no-change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no-change classes. The preliminary results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of the these solutions were of 0.82 while the false alarm rates were 2.7%. The initial findings indicated that the accuracy of the change result obtained using Kittler-Illingworth algorithm varies depending on how the assumed conditional class density function fits the histograms of change and no-change classes.

Fusion of SAR and optical data for unsupervised change detection: A case study in Beijing

Change detection can either be carried out using multitemporal optical or synthetic aperture radar (SAR) images. Due to the different electromagnetic spectrum used, these two types of imagery provide different representations of the same physical reality. Change information extraction can benefit from the fusion of SAR and optical data. In this paper we investigate the fusion of SAR and optical for change detection application. Beijing, the capital of China that has experienced rapid urbanization, is selected as a case study. Two multitemporal datasets that consist of Landsat and SAR (ERS-2 and ENVISAT) images are used. An unsupervised classification framework that combines the virtues of the k-mean and SVM supervised classifier is proposed. Different fusion strategies are tested including fusion at the feature level and at the decision level. The analysis reveals that the best result can be obtained when the fusion of change information is carried out at the decision level.

Object-based change detection in urban areas using multitemporal high resolution SAR images with unsupervised thresholding algorithms

With the recent launches of optical and SAR systems that are capable of producing images in very high resolution, the quantification of temporal changes can be achieved with unprecedented level of details. However, very high resolution data presents new challenges and difficulties such as the strong intensity variations within land cover classes thus the noisy appearance of change map generated by pixel-based change detection. This has led to the development of object-based approaches that utilize image segmentation. For unsupervised change detection, on the other hand, automatic thresholding algorithms provided a simple yet effective technique to produce a binary change map. Thresholding techniques have been used successfully for pixel-based change detection using medium resolution SAR images. They have also been used for object-based change detection using high resolution optical imagery. However, they have not been tested in the context of object-based change detection using high resolution SAR images. Therefore, this chapter investigates the potential of several thresholding techniques for object-based unsupervised detection of urban changes using high resolution SAR images. To avoid the creation of sliver polygons, the multidate image segmentation strategy is adopted to produce image objects that are spectrally, spatially, and temporally homogeneous. A change image is generated by comparing objects multitemporal mean intensities using the modified ratio operator. To threshold the change image and generate a binary change map, three thresholding algorithms, i.e., the Kittler-Illingworth algorithm, the Otsu method, and the outlier detection technique, are tested and compared. Two multitemporal datasets consisting of TerraSAR-X images acquired over Beijing and Shanghai are used for evaluation. Quantitative and qualitative analyses reveal that the three algorithms achieved similar results. The three algorithms achieved Kappa coefficients around 0.6 for the Beijing dataset and 0.75 for the Shanghai datasets. The analysis also reveals the limitation of the mathematical comparison operator in accentuating the difference between the changed and the unchanged class, thus calls for the development of more sophisticated object-based change image generation mechanisms capable of reflecting all types of changes in the complex urban environment.