Qiuze Yu

An improved Normalized Cross Correlation algorithm for SAR image registration

This paper proposes a robust and fast matching method based on Normalized Cross Correlation (NCC) for Synthetic Aperture Radar (SAR) image matching. NCC is a robust algorithm in SAR image matching. Two main drawbacks of the NCC algorithm are the flatness of the similarity measure maxima, due to the self-similarity of the images, and the high computational complexity [1]. To tackle these two problems, we adopt the block partitioning strategy, texture feature analysis, and the Fast Fourier Transformation (FFT) algorithm and Integral Images to improve the performance of the conventional NCC algorithm. In the block partitioning strategy, we divide the template and the corresponding sub-window in the examined image into some sub-blocks, and there are several sub-blocks in the template, then we use texture features to increase the weight of sub-blocks which contain more terrain information in the template during the matching process, in this way we improve the flatness of the similarity measure maxima greatly. After that we use the FFT algorithm and Integral Images to speed up the proposed method, with the actual situation of our experiment we adopt the FFT and Integral Images based on the block partitioning strategy, thus we significantly reduce the number of computations required to carry out template matching based on the conventional NCC. Experimental results show that the proposed algorithm is more robust and faster than the conventional NCC algorithm.

Framework design and implementation for oil tank detection in optical satellite imagery

In this paper, we propose a coarse-to-fine framework design and implementation for oil tank detection in optical satellite imagery. The framework is mainly composed of two operations: 1) from the whole scene imagery, extraction of patches with oil tanks based on the probabilistic latent semantic analysis model; 2) in the relatively small size patches, detection of the oil tanks with Hough transform and template matching. Experiments show that the framework provides a promising solution for oil tank detection in optical satellite imagery.

A Spy Positive and Unlabeled Learning classifier and its application in HR SAR image scene interpretation

In this paper, we present a Spy Positive and Unlabeled Learning (SPUL) classifier. It is a novel two-step strategy of implementing a positive-and-unlabeled-sample-based classifier. In the first step, by using spy detection, the unlabeled samples are divided into unreliable positive and reliable negative samples. In the second step, the classifier is built using labeled positive, unreliable positive, and reliable negative samples with different and suitable weights. The proposed SPUL classifier is incorporated into a One-Class-Extraction (OCE) framework for High Resolution (HR) Synthetic Aperture Radar (SAR) image scene interpretation. The performance of the SPUL classifier and the SPUL-based OCE framework is presented and analyzed on a TerraSAR-X HR SAR image.

Context-aware information modeling for HR SAR image scene interpretation

In this paper, we improve the traditional bag-of-words-based image representation method in two aspects: preserving the semantics in vocabulary generation and incorporating spatial relations in image representation. Based on that, we present a novel context-aware information modeling method for high resolution synthetic aperture radar image scene interpretation. We compare the proposed method with traditional ones in scene interpretation on TerraSAR-X data sets.

Coastline detection in SAR images using multi-feature and SVM

Coastline detection in synthetic aperture radar (SAR) images is difficult and important. As we all know, water areas in SAR images are much more homogeneous in grey levels than land regions. Therefore, features reflecting the texture of an image can be very useful for water-land separation. Traditional methods mainly view gray-scale as a critical rule to detect coastline from background. In this paper, we pay more attention to the characteristics of the different texture between water and non-water objects. A novel method based on circular-window gray feature and gray level co-occurrence matrix (GLCM) is proposed. Eighteen features of water and non-water areas depicted by circular-window gray feature and GLCM are fed into a support vector machine (SVM) classifier to extract coastline. The experimental results demonstrate that the proposed approach has better performance compared with other ones.1

An automatic global-to-local image registration based on SIFT and thin-plate spline (TPS)

For remote sensing applications, automatic image registration is an essential part for further image processing such as image fusion, change detection and so on. In this paper, we propose an effective and fast automatic image registration based on both global and local method. In the first stage, we extract SIFT features and make the affine transformation with RANSAC, a robust outlier removal method. After the image globally registered, uniform spacing control points are selected in the common area. Through template matching, control points are refined and more local to the image. At last thin-plate spline, a nonrigid image registration transformation function is applied to achieve local matching. The experiment results indicate that this method not only distinctive and robust in overall registration but also significantly improve matching performance in local area1.

An improved particle swarm optimization algorithm for high dimension image matching

This paper proposes an optimized and efficient matching method based on Particle Swarm Optimization (PSO) for image matching. PSO is an efficient intelligent algorithm in image matching. It is a kind of stochastic optimized algorithm developed by Eberhart and Kennedy in 1995. In this paper, the application of PSO is focused on image matching in 3 dimensions with variant angles. The ordinary template matching for the 3 dimensions image matching involves large computational complexity. PSO has been improved in the aspect of self-adaption for convergence. Combining PSO with the individual intelligence, the computation and error rate have been significantly reduced. An extended part of PSO algorithm called multi-swarms is introduced. The multi-swarms PSO (MPSO) is applied to the multi-targets matching in the high dimension space. The performance of MPSO is satisfactory due to the interaction between different swarms such as repulsion and convergence. The Experiments results show that Particle Swarm Optimization Algorithm is much faster in the image matching tasks. MPSO has a good performance in multi-targets matching which involves huge computation complexity.

Bayesian change detection based on space contextual information and EM algorithm

In this paper, an unsupervised change detection method based on space contextual information and EM algorithm is proposed. In the algorithm, each pixel of the difference image is represented by a characteristic quantity constructed from the difference image values considering the space contextual information. EM algorithm is used to achieve the parameter estimation of each class pixels. Bayesian inference is then employed to perform the final change detection results. Experimental results obtained on multi-temporal optical images acquired by Landsat 5 TM confirm the effectiveness of the proposed approach.

Land-cover classification of SAR images by combining low-level features and category context

A novel land-cover classification framework for HR SAR images which combines low-level features and category context is presented in this paper. We use patch-based features for low-level information extraction, including average intensity, texture within a patch and the super texture we proposed to model the texture similarity of neighboring patches. To represent the local category context of SAR images, we propose the label layout filter. This work resolves local ambiguities of low-level features from a category context perspective. The framework demonstrates good performance in both accuracy and visual appearance for HR SAR scene interpretation.

Concurrent SAR images denoising and segmentation based on a novel model of wavelet coefficients

A novel segmentation algorithm for Synthetic Aperture Radar (SAR) images is presented in this paper to improve performance. First, we design a model of wavelet coefficients based on the relativities of the coefficients at different scales to suppress noise. Furthermore, we employ a weight-variant graph-cuts-based approach to extract objects from complex background. Finally, we compare our proposed algorithms with several segmentation measures on synthetic and real SAR images and the experimental results demonstrate that the proposed strategies have better performances in speckle suppression and image segmentation compared with other methods.