Haigang Sui

Automatic Optical-to-SAR Image Registration by Iterative Line Extraction and Voronoi Integrated Spectral Point Matching

Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods strongly depends on feature extraction results. In this paper, a new method based on iterative line extraction and Voronoi integrated spectral point matching is developed. The core idea consists of three aspects: 1) An iterative procedure that combines line segment extraction and line intersections matching is proposed to avoid registration failure caused by poor feature extraction. 2) A multilevel strategy of coarse-to-fine registration is presented. The coarse registration aims to preserve main linear structures while reducing data redundancy, thus providing robust feature matching results for fine registration. 3) Voronoi diagram is introduced into spectral point matching to further enhance the matching accuracy between two sets of line intersection. Experimental results show that the proposed method improves the matching performance. Compared with previous methods, the proposed algorithm can effectively and robustly generate sufficient reliable point pairs and provide accurate registration.

A novel multi-scale level set method for SAR image segmentation based on a statistical model

To overcome the problems of large data volumes and strong speckle noise in synthetic aperture radar (SAR) images, a multi-scale level set approach for SAR image segmentation is proposed in this article. Because the multi-scale analysis of SAR images preserves their highest resolution features while additionally making use of sets of images at lower resolutions to improve specific functions, the proposed method is useful for removing the influence of speckle and, at the same time, preserving important structural information. The Gamma distribution is one of the most commonly used models employed to represent the statistical characteristics of speckle noise in a SAR image and it is introduced to define the energy functional. Moreover, based on the multi-scale level set framework, an improved multi-layer approach is introduced for multi-region segmentation. To obtain a fast and more accurate result, a novel threshold segmentation result is used to represent the initial segmentation curve. The experiments with synthetic and real SAR images demonstrate the effectiveness of the new method.

Road extraction from SAR imagery based on an improved particle filtering and snake model

This article presents an improved approach for road extraction from synthetic aperture radar (SAR) imagery. First, an improved fast road detector is used to obtain road response and direction, as well as to reduce false alarm rates resulting from bright line objects. Particle filtering is then applied in selecting road seed points, and particle weight is designed on the basis of the road characteristics derived from SAR images. Finally, a snake model is used to connect the seed points to form a road. Results show that the proposed method is effective, with enhanced completeness, correctness, and quality even under the influence of high-backscattering context objects near roads.

An automatic optical and SAR image registration method with iterative level set segmentation and SIFT

Although optical image registration methods have been successfully developed over the past decades, the registration of optical and synthetic aperture radar (SAR) images is still a challenging problem in remote sensing. Feature-based methods are considered to be more effective for multi-source image registration. However, almost all of these methods rely on the feature extraction algorithms. In this article, a simultaneous segmentation and feature-based registration method based on an iterative level set and scale-invariant feature transform (ILS-SIFT) is proposed. The core idea consists of three aspects: (1) an iterative procedure that combines image segmentation and matching is proposed to avoid registration failure caused by poor feature extraction; (2) a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features; and (3) an improved SIFT algorithm is employed to determine whether the registration was successful. Experimental results have shown the effectiveness and universality of the proposed method.

Automatic image registration based on convexity model and full-scale image segmentation

Image registration plays a critically important role in many practical problems in diverse fields. A new object-oriented image matching algorithm is presented based on the convexity model (CM) and full-scale image segmentation. The core idea of this matching algorithm is to use image objects as matching unit rather than points or lines. This algorithm firstly converts images into image objects trees by full-scale segmentation and convexity model restriction. Because image objects which accord with the convexity model have rich and reliable statistical information and stable shapes, more characteristics can be used in object-based image matching than pixel-based image matching. Initial experiments show that matching algorithm proposed in this paper is not sensitive to rotation and resolution distortion, which can accomplish the image matching and registration automatically.

Detecting building façade damage from oblique aerial images using local symmetry feature and the Gini Index

ABSTRACT The Classification of damaged building types is currently a relevant topic in disaster assessment and management, and the detection of damaged building facades is important to improve the classification accuracy. In this letter, a novel approach for automatic detection of damaged facade based on local symmetry features and the Gini Index using oblique aerial images is presented. First, local symmetry points are detected in a sliding window. Then, we obtain histogram bins of local symmetrical points in the vertical and horizontal directions. Finally, damaged and undamaged of building facades are distinguished using Gini Index. An evaluation of experimental result, for a selected Beichuan earthquake ruins study site, in Sichuan, China, shows that this method is feasible and effective for the detection of damaged facades.

A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection

Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA)—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images.

Copula-Based Joint Statistical Model for Polarimetric Features and Its Application in PolSAR Image Classification

Polarimetric features are essential to polarimetric synthetic aperture radar (PolSAR) image classification for their better physical understanding of terrain targets. The designed classifiers often achieve better performance via feature combination. However, the simply combination of polarimetric features cannot fully represent the information in PolSAR data, and the statistics of polarimetric features are not extensively studied. In this paper, we propose a joint statistical model for polarimetric features derived from the covariance matrix. The model is based on copula for multivariate distribution modeling and alpha-stable distribution for marginal probability density function estimations. We denote such model by CoAS. The proposed model has several advantages. First, the model is designed for real-valued polarimetric features, which avoids the complex matrix operations associated with the covariance and coherency matrices. Second, these features consist of amplitudes, correlation magnitudes, and phase differences between polarization channels. They efficiently encode information in PolSAR data, which lends itself to interpretability of results in the PolSAR context. Third, the CoAS model takes advantage of both copula and the alpha-stable distribution, which makes it general and flexible to construct the joint statistical model accounting for dependence between features. Finally, a supervised Markovian classification scheme based on the proposed CoAS model is presented. The classification results on several PolSAR data sets validate the efficacy of CoAS in PolSAR image modeling and classification. The proposed CoAS-based classifiers yield superior performance, especially in building areas. The overall accuracies are higher by 5%–10%, compared with other benchmark statistical model-based classification techniques.

Metric learning based collapsed building extraction from post-earthquake PolSAR imagery

In this paper we proposed a metric learning-based method to extract collapsed buildings from post-earthquake PolSAR imagery. In this method, eight building and orientation related features, including entropy H, the average scattering angle α, anisotropy A, the circular polarization correlation coefficient ρ and the four scattering powers of Yamaguchi 4 component decomposition with a rotation of the coherency matrix, are considered and analyzed. Then a transformation matrix is learned from collapsed and intact building samples via an improved informational-theoretic metric learning(ITML). With such a transformation matrix, the features are projected into a low-dimension space to mitigate the impact of topography and buildings aspect angle. Finally a k - NN classifier is utilized to distinguish collapsed and intact buildings. The proposed method is tested on one RadarSAT-2 PolSAR image acquired after 2010 Yushu Earthquake in the Qinghai Province of China. Results are validated by the manually interpretation map of a very high resolution (VHR) optical image. It shows that, the method is efficient to extract collapsed building areas using limited samples and only one post-earthquake PolSAR image.

Detection of Damaged Rooftop Areas From High-Resolution Aerial Images Based on Visual Bag-of-Words Model

The classification of damaged building types has received increasing attention in recent years. The detection of damaged rooftop areas is crucial to improve the accuracy of classification of building damaged types. In this letter, an approach for the automatic detection of damaged rooftops areas based on the visual bag-of-words (BoWs) model is presented. First, the building rooftop is segmented into different superpixel areas. Then, the visual BoWs model is employed to build semantic feature vectors for damaged or nondamaged parts of each superpixel area. Finally, damaged and nondamaged parts of rooftop superpixel areas are discriminated using support vector machine. An evaluation of experimental results, for a selected study site of the Beichuan earthquake ruins, Sichuan, China, shows that this method is feasible and effective for the detection of damaged rooftop areas.