Ziyi Chen

Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels

This paper presents a study of vehicle detection from high-resolution aerial images. In this paper, a superpixel segmentation method designed for aerial images is proposed to control the segmentation with a low breakage rate. To make the training and detection more efficient, we extract meaningful patches based on the centers of the segmented superpixels. After the segmentation, through a training sample selection iteration strategy that is based on the sparse representation, we obtain a complete and small training subset from the original entire training set. With the selected training subset, we obtain a dictionary with high discrimination ability for vehicle detection. During training and detection, the grids of histogram of oriented gradient descriptor are used for feature extraction. To further improve the training and detection efficiency, a method is proposed for the defined main direction estimation of each patch. By rotating each patch to its main direction, we give the patches consistent directions. Comprehensive analyses and comparisons on two data sets illustrate the satisfactory performance of the proposed algorithm.

Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds

Semantic labeling of road scenes using colorized mobile LiDAR point clouds is of great significance in a variety of applications, particularly intelligent transportation systems. However, many challenges, such as incompleteness of objects caused by occlusion, overlapping between neighboring objects, interclass local similarities, and computational burden brought by a huge number of points, make it an ongoing open research area. In this paper, we propose a novel patch-based framework for labeling road scenes of colorized mobile LiDAR point clouds. In the proposed framework, first, three-dimensional (3-D) patches extracted from point clouds are used to construct a 3-D patch-based match graph structure (3D-PMG), which transfers category labels from labeled to unlabeled point cloud road scenes efficiently. Then, to rectify the transferring errors caused by local patch similarities in different categories, contextual information among 3-D patches is exploited by combining 3D-PMG with Markov random fields. In the experiments, the proposed framework is validated on colorized mobile LiDAR point clouds acquired by the RIEGL VMX-450 mobile LiDAR system. Comparative experiments show the superior performance of the proposed framework for accurate semantic labeling of road scenes.

Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature

This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and a multiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.

Fast and noninterpolating method for subpixel displacement analysis of digital speckle images using phase shifts of spatial frequency spectra

A fast noninterpolation method for calculating displacement of digital speckle images with subpixel precision was introduced. In this method, the precise displacement is obtained from phase shifts of spatial frequency spectra of two digital speckle images instead of digital correlation calculation. First, digital speckle images before and after displacement are windowed and fast Fourier transform is performed. Then, phase shifts of different spatial frequencies are linearly fitted in spectral space using the least square method, and a coarse displacement value is directly calculated according to the phase shift theorem of Fourier transform. By a window technique and iterative procedure, the influence of finite image size on the accuracy of the results is eliminated, and the accurate displacement is obtained finally. It is significant that the method obtains the subpixel-precision displacement without any interpolation operations. The test results show that the method has high computing efficiency, high precision, and good robustness to low image quality.

OIL SPILL DETECTION BASED ON A SUPERPIXEL SEGMENTATION METHOD FOR SAR IMAGE

In this paper, a rapid oil spill detection approach which still maintains high detection accuracy is presented. The major contribution of the approach is using a superpixel segmentation method to subdivide the target SAR image into many approximate uniform scale pieces and preserves the boundaries well. Furthermore, a novel approach combine space distance, intensity deviation and size information together (SIS) is presented to eliminate the potential false positive, which is convenient and effective meanwhile. The proposed approach performs well and fast in both the synthetic data and RAD ARS AT-1 ScanSAR data which contain verified oil spills. The processing time is about 6s for a 512×512 image.

Vehicle Detection from Remote Sensing Image Based on Superpixel Segmentation and Image Enhancement

Automatic vehicle detection from high-resolution remote sensing image is a challenging topic. While there have been some studies on this topic in recent years, a fast and robust approach is yet to be found, especially when facing the scenario of low color contrast. In this paper, a new vehicle detection approach is proposed. First, superpixel-based segmentation is used to identify potential vehicle regions to speed up the detection and improve the accuracy. Then, an image enhancement method is also proposed, which greatly improves the segmentation results. Support vector machine is used to classification with features extracted by HOG descriptor. According to the experiments, by combining with superpixel segmentation and the image enhancement, the speed of the vehicle detection is improved with approximately an order of magnitude. Also, in case of low contrast remote sensing images, the detection accuracy can be also greatly improved, with much less false positives and false negatives.

Exploiting location information to detect light pole in mobile LiDAR point clouds

With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false alarm. In the proposed method, the potential light poles are first detected by the extended Hough Forest Framework. Then, a gaussian distribution is exploited to model the distance between two light poles by using locations of those detected light poles. Finally, inaccurately detected light poles are removed by considering the distance between two adjacent objects. We evaluate our proposed method on mobile LiDAR point clouds acquired by RIEGL VMX-450 system. On the basis of the experimental test instances, we demonstrate improved accuracy on light pole detection.

Vehicle detection from high-resolution aerial images based on superpixel and color name features

Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homogeneous patches, which consist of the basic units during the detection to improve efficiency. By introducing the sparse representation into our method, powerful classification ability is achieved after the dictionary training. To effectively describe a patch, the Histogram of Oriented Gradient (HOG) is used. We further propose to integrate color information to enrich the feature representation by using the color name feature. The final feature consists of both HOG and color name based histogram, by which we get a strong descriptor of a patch. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm for vehicle detection from aerial images.