Lizhong Xu

A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks.

A kernel-based block matrix decomposition approach for the classification of remotely sensed images

The classification problem of remotely sensed images with hyperspectral and hyperspatial resolution images is being paid more and more attention. The success of remotely sensed images classification depends on many facts, such as the availability of high-quality images and ancillary data, proper classification procedure, and the analytical ability of scientific researcher. Therefore, lots of methods of combing spatial, spectral and texture information were proposed. However, these methods may ignore these facts as below. On the one hand, many details of the original remotely sensed images may be covered up by the too much overlapping information. On the other hand, the classification process is time-consuming. Therefore, a new and efficient classification of remotely sensed images method is introduced to overcome these shortcomings. The proposed method deals with the original information provided by the remotely sensed images is considered. The block matrix is made of training samples of the same class. The details of original remotely sensed images is obtained from the QR decomposition with column pivoting (QRcp) or singular value decomposition (SVD). And then, using fisher linear discriminant analysis (FLDA) methods, the projection data information of original remotely sensed images is jointly used for the classification through a support vector machines (SVMs) formulation. Experiments on hyperspatial and hyperspectral images are performed to test and evaluate the effectiveness of the proposed method.

Spatiotemporal difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes

Tracking infrared (IR) small targets is a vital component of many computer vision applications, including IR precise guidance, early warning, and IR remote sensing. Various complicated scenes, however, present significant challenges to the tracking task. To solve this problem, we present a novel 3D spatiotemporal difference-of-Gaussians (DoG) filter-based algorithm for tracking small targets in IR videos of various complex scenes. First, biologically inspired 3D DoG filters are proposed for IR small target tracking, which are capable of accounting for spatial and temporal information. Then, based on such filters, an effective and robust tracker is constructed to track the small targets, which are spatiotemporally distinguishable from background clutter. Extensive experiments show that our approach tracks the small targets accurately and robustly in realistic IR videos of complex backgrounds that present unique difficulties to other approaches.

An Extraction method for Water Body of Remote Sensing Image Based on Oscillatory Network

How to avoiding the disadvantage of the pixel-wise image process methods has been a open problem. An object-wise method was proposed to extract the water body of remote sensing image, which is locally excitatory globally inhibitory oscillator networks (LEGION). This oscillation network could bind an object based on the similar property. The oscillation network in this paper used the normalized difference water index (NDWI) to encode the binding of pixels of water body. Because of the good coherence of spectrum of water body on the remote sensing image, the pixels of the water surface have strong couple weight and can overcome the globally inhibition in the network. Additionally the special spectrum property also was considered, it could reduce the disturber of the other ground object which has the similar spectra property. The proposed method combined both pixel-wise and object-wise scale. Thus, the proposed extraction method was robust to noise, and could keep the detail information of water body. The outputs were according to the human perception mechanism and can be used in the higher image analysis and reorganization. The results of experiment validate the effective of the proposed method.

A spectral---textural kernel-based classification method of remotely sensed images

Most studies have been based on the original computation mode of semivariogram and discrete semivariance values. In this paper, a set of texture features are described to improve the accuracy of object-oriented classification in remotely sensed images. So, we proposed a classification method support vector machine (SVM) with spectral information and texture features (ST-SVM), which incorporates texture features in remotely sensed images into SVM. Using kernel methods, the spectral information and texture features are jointly used for the classification by a SVM formulation. Then, the texture features were calculated based on segmented block matrix image objects using the panchromatic band. A comparison of classification results on real-world data sets demonstrates that the texture features in this paper are useful supplement information for the spectral object-oriented classification, and proposed ST-SVM classification accuracy than the traditional SVM method with only spectral information.

Stereo depth estimation under different camera calibration and alignment errors

Depth estimation is a fundamental issue in computational stereo. To obtain accurate stereo depth estimation, all mechanical parameters with a high precision need to be measured in order to achieve subpixel accuracy and to match features between two different images. This paper investigates accurate depth estimation with different mechanical parameter errors, such as camera calibration and alignment errors, which mainly result from camera lens distortion, camera translation, rotation, pitch, and yaw. For each source of the errors, a model for the error description is presented, and the accurate depth estimation due to this error is quantitatively analyzed. Depth estimation algorithms under an individual error, and with all the errors, are given. Experimental results show that the proposed models can rectify the errors and calculate the accurate depths effectively.

Multispectral and panchromatic image fusion based on improved bilateral filter

Image fusion is of great importance to various remote sensing applications because many Earth observation satellites provide both high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images. A number of fusion methods have been proposed, such as intensity-hue-saturation fusion and wavelet transform fusion methods. However, further studies are still necessary to improve the fusion performance for new types of remotely sensed images, such as IKONOS or QuickBird images. We propose an improved bilateral total variation filter method for fusing such MS and Pan images based on regularization. First, the constraints on the MS and Pan images are imposed based on the observation model. Then, the improved bilateral filter is used as an a priori model to constrain the high-resolution MS images. Finally, the steepest descent optimization algorithm is used to obtain the estimated MS images. Fusion simulations on spatially degraded IKONOS and QuickBird images, whose original MS images are available for reference, respectively, show that the proposed approach has better spatial quality while keeping the spectral information of the MS images.

A Special Issue of Intelligent Automation and Soft Computing: Intelligent Information Processing And System Optimization

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A J2ME-Based Wireless Intelligent Video Surveillance System Using Moving Object Recognition Technology

A low-cost intelligent mobile phone-based wireless video surveillance solution using moving object recognition technology is proposed in this paper. The proposed solution can be applied not only to various security systems, but also to environmental surveillance. Firstly, the basic principle of moving object detecting is given. Limited by the memory consuming and computing capacity of a mobile phone, a background subtraction algorithm is presented for adaptation. Then, a self-adaptive background model that can update automatically and timely to adapt to the slow and slight changes of natural environment is detailed. When the subtraction of the current captured image and the background reaches a certain threshold, a moving object is considered to be in the current view, and the mobile phone will automatically notify the central control unit or the user through phone call, SMS (Short Message System) or other means. The proposed algorithm can be implemented in an embedded system with little memory consumption and storage space, so it’s feasible for mobile phones and other embedded platforms, and the proposed solution can be used in constructing mobile security monitoring system with low-cost hardware and equipments. Based on J2ME (Java2 Micro Edition) technology, a prototype system was developed using JSR135 (Java Specification Requests 135: Mobile Media API) and JSR120 (Java Specification Requests 120: Wireless Messaging API) and the test results show the effectiveness of proposed solution.

Multiple Feature Fusion for Tracking of Moving Objects in Video Surveillance

Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion scheme. The scheme randomly selects single observation model to evaluate the likelihood of some particles. The stochastic selection probability is adjusted adaptively by the uncertainty associated with a feature model. The experiment shows that the proposed method has strong tracking robustness and can effectively solve the occlusion problem.