Guangluan Xu

An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature–based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.

Aircraft recognition in high resolution SAR images using saliency map and scattering structure features

Scattering structure features of targets is of great importance for Synthetic Aperture Radar (SAR) image analysis. In this paper, a novel algorithm for aircraft recognition in high resolution apron area of SAR images is proposed. The algorithm combines the strength of gradient saliency map and scattering structure features to improve accuracy and efficiency. Specially, Constant False-Alarm Rate (CFAR) algorithm is carried out to segment images. Then, a new efficient object locating method based on directional local gradient map is proposed to detect aircraft targets. Then, the candidate slices as well as template slices are modeled using Gaussian Mixture Model (GMM), which will be treated as structure features. In the recognition stage, a novel similarity measurement algorithm based on Kullback-Leibler Divergence for GMM models is proposed for classification. We conduct experiments on the dataset with 3.0m resolution and the recognition results demonstrate the accuracy of our proposed method.

Scene classification in remote sensing images using a two-stage neural network ensemble model

ABSTRACT Scene classification has long been a challenging task in the remote sensing field. Conventional approaches based on hand-crafted features are not suitable in large scale remote sensing images. Convolutional Neural Network (CNN) achieves great success in computer vision field by learning hierarchical features automatically from mass data. However, the shortage of labeled dataset in remote sensing field results in severe overfitting and the ensemble of several networks have better generalization ability than one single network. In this letter, we propose a novel Two-Stage Neural Network Ensemble Model to solve the problems mentioned above. Firstly, to overcome overfitting, we pre-train a CNN using the ImageNet dataset and fine tune the network by labeled remote sensing images. Then, the output features are fed to a Restricted Boltzmann Machine (RBM) Retrained Network to get better feature representations. Finally, in testing stage, a method based on Ensemble Inference Network (EIN) is introduced to enhance the generalization ability by combining the classification results of several networks. Experimental results on the UC Merced Land Use (UCML) dataset demonstrate the effectiveness of our proposed method.

Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.

Synthesizing remote sensing images by conditional adversarial networks

Automated annotation of urban areas from overhead imagery plays an essential role in many remote sensing applications. Generative Adversarial Nets (GANs) is one of the most effective ways to handle this problem. In this manuscript, two tricks were added in conditional GANs(cGANs) which learn the mapping from input image to output remote sensing image. All the experimental results demonstrated that cGANs was a reliable way to generate high-quality remote sensing images. Whats more, when this method be applied to semantic segmentation and accurate classification was made by using ISPRS 2D semantic labelling challenge dataset.

Model selection for high resolution InSAR coherence statistics over urban areas and its application in building detection

The interferometric coherence map is derived from the cross-correlation of two registered synthetic aperture radar (SAR) images. It can give additional information complementary to the intensity image, or act as an independent information source in many applications. Compared to the plenty of work on SAR intensity statistics, there are quite fewer researches on the statistical characters of interferometric SAR (InSAR) coherence. And to our knowledge, all of the existing work that related to InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. Our main contribution is the investigation on the accuracies of several typical models for high resolution coherence statistics over urban areas. We select three typical land classes including trees, buildings, and shadow, as the representatives of urban areas. And different models including Gaussian, Weibull, Rayleigh, Nakagami and Beta are evaluated. Experiment results on TanDEM-X data illustrate that the Beta model reveals a better performance than other distributions. Finally, the Beta model is used in the detection of buildings.

Comparison of recent non-linear filters from graphics field

Image filtering is an important and fundamental issue in image processing pipelines and find itself a lot of applications in segmentation, salient features detection, colorization, stylization and so on. In recent years, several nonlinear filters aiming at edge-preserving smoothing has been proposed from different fields. However, none of these filters is perfect for all applications due to their own model assumption and solving strategy. In this paper, we give a brief introduction to several of them particularly from graphics field and comparison about their advantages and limitations through experiments. We look forward to offer an helpful starting point for researchers to select or improve them.