A genetic algorithm to simultaneously retrieve land surface roughness and soil wetness

Two key parameters to affect microwave backscattering from the land surface are the surface roughness and soil wetness. A novel genetic algorithm is developed for multi-parameter retrieval of land surface roughness and soil wetness from angular backscattering observations. Parameters of wetness and roughness are encoded into genes. Genes are constituents of chromosomes, which undergo optimal selection based on a natural evolutionary process in the genetic algorithm. The theoretical model of a two-scale rough surface is employed for computation of the cost function. Results retrieved using this genetic algorithm are compared well with ground data measurements. This study presents an example of the genetic algorithm for application of multi-parameter retrieval in remote sensing.     

Operational bi-angle approach to retrieve the Earth surface albedo from AVHRR data in the visible band

The inference of surface spectral reflectance using visible observations is complicated mainly because of scattering effects. In the present paper we attempt a solution to the problem of retrieval of surface reflectance from satellite radiance measurements based on a solution of the radiative transfer equation. We have developed an operational method which relies on multiple view angle observations or multiple solar zenith angle observations of the surface to accomplish part of this task in a routine manner. This approach may be used for ERS-2 ATSR visible bands because there will be two view angle observations for the same area at essentially the same time.

This approach can also be used with NOAA satellite AVHRR data by assuming that the distribution of the aerosol does not vary too rapidly as a function of time. Usually it is better to select the AVHRR data from around noon and at dusk in the same day. This approach requires that the visibility of the area should be more than 5 km for multiple solar zenith angle observations applications.     

Detecting motion patterns via direction maps with application to surveillance

Detection of motion patterns in video data can be significantly simplified by abstracting away from pixel intensity values towards representations that explicitly and compactly capture movement across space and time. A novel representation that captures the spatiotemporal distributions of motion across regions of interest, called the “Direction Map,” abstracts video data by assigning a two-dimensional vector, representative of local direction of motion, to quantized regions in space-time. Methods are presented for recovering direction maps from video, constructing direction map templates (defining target motion patterns of interest) and comparing templates to newly acquired video (for pattern detection and localization). These methods have been successfully implemented and tested (with real-time considerations) on over 6300 frames across seven surveillance/traffic videos, detecting potential targets of interest as they traverse the scene in specific ways. Results show an overall recognition rate of approximately 91% hits vs 8% false positives.     

Indecomposable local maps of tessellation automata

Indecomposable local maps of one-dimensional tessellation automata are studied. The main results of this paper are the following. (1) For any alphabet containing two or more symbols and for anyn 1, there exist indecomposable scope-n local maps over . (2) If is a finite field of prime order, then a linear scope-n local map over is indecomposable if and only if its associated polynomial is an irreducible polynomial of degreen – 1 over , except for a trivial case. (3) Result (2) is no longer true if is a finite field whose order is not prime.     

A local space–time kriging approach applied to a national outpatient malaria data set

Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya (the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model. Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%).     

Recursive estimation of 3D motion and surface structure from local affine flow parameters

A recursive structure from motion algorithm based on optical flow measurements taken from an image sequence is described. It provides estimates of surface normal in addition to 3D motion and depth. The measurements are affine motion parameters which approximate the local flow fields associated with near-planar surface patches in the scene. These are integrated over time to give estimates of the 3D parameters using an extended Kalman filter. This also estimates the camera focal length and, so, the 3D estimates are metric. The use of parametric measurements means that the algorithm is computationally less demanding than previous optical flow approaches and the recursive filter builds in a degree of noise robustness. Results of experiments on synthetic and real image sequences demonstrate that the algorithm performs well.     

一种运动估计的自适应菱形算法

在菱形搜索算法的基础上,依据图像序列的运动矢量的时空相关性和中心偏移特性,首先对宏块进行类型划分、设定阀值,进一步提出了初始搜索点的预测。实验证明,该算法在保证图像质量的同时,大大提高了搜索速度。     

Developed split window method to retrieve land surface temperature from HYTES thermal hyperspectral images using genetic algorithm

This article aims to establish a new method to retrieve land surface temperature (LST) from hyperspectral thermal emission spectrometer (HYTES) data with split window (SW) algorithm. First, the optimal bands of HYTES sensor were selected with the genetic algorithm and then were used in the SW algorithm. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapours (CWVs) and view zenith angle (VZA) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the root-mean-square error (RMSE) varies for different CWV and VZA, and with the increasing CWV and VZA, the RMSE value also increases. The emissivity, CWV, and VZA were also obtained for pixels. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapour demonstrated the good performance of the proposed algorithm. Finally, the new algorithm was applied to HYTES sensor data, and the LST was validated using LST product of HYTES sensor obtained by NASA. The results showed that the RMSE of the LST retrieval with the proposed algorithm and the LST product of sensor for data 1 and data 2 is 1.3 K and 1.6 K, respectively.     

Adaptive unified motion control of mobile manipulators

This paper presents a unified motion controller for mobile manipulators which not only solves the problems of point stabilization and trajectory tracking but also the path following problem. The control problem is solved based on the kinematic model of the robot. Then, a dynamic compensation is considered based on a dynamic model with inputs being the reference velocities to the mobile platform and the manipulator joints. An adaptive controller for on-line updating the robot dynamics is also proposed. Stability and robustness of the complete control system are proved through the Lyapunov method. The performance of the proposed controller is shown through real experiments.     

参数未知的多个操作器的一种自适应控制算法

在多个操作器的内力与运动的控制中,内力控制是主要的挑战.在假设接触模型为点接触的前提下,讨论了参数未知的多个操作器内力的自适应控制.先给出多个操作器所成系统具有的一些特性;其次导出了内力误差与动力学参数间的关系;最后在条件PE(persistent-exciting)不成立的情况下,给出了一种保证内力收敛于零的复合控制.由理论证明及仿真实验可知,本文给出的控制器既使被操作的物体运动轨迹渐进跟踪期望的运动轨迹,又使作用在物体上的内力渐进跟踪期望的内力.     

Reorganizing local image features with chaotic maps: an application to texture recognition

Multimedia Tools and Applications - Texture images are those where the focus of the analysis is on the spatial arrangement of pixels (primitives or textons) rather than on particular objects in the...     

Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

This article uses powerful technique of artificial neural network (ANN) models to simulate and estimate structural response of two-storey shear building by training the model for a particular earthquake. The neural network is first trained for a real earthquake data and the numerically generated responses of different floors of two-storey buildings as the training patterns. Trained ANN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by ANN model are good for practical purposes. It is worth mentioning that although the simulation is done with numerically generated response data for particular earthquake, the idea may also be used for actual experimental (response) data.     

Adaptive motion control law of a robotic wheelchair

In the present work, a dynamic model of a robotic wheelchair is developed considering a lateral deviation of the center of mass. The Lyapunov and input/output stability theories are used to design a novel tracking and positioning adaptive control for the robotic wheelchair. Properties of the dynamic model with respect to its matrices and parameters are shown. A filter is used to obtain a closed loop equation that allows designing the adaptive control law. Then, a projection algorithm is used to improve the adaptive control in the sense of avoiding parameter drift. Experimental results show good performance of the adaptive control.     

Adaptive self-organized maps based on bidirectional approximate reasoning and its applications to information filtering

Similarity measuring is one substantial part in self-organizing maps (SOM) for its direct influence on the mapping results. The common used similarity measuring method – Euclidean distance cannot always express the exact similarity. In this paper, a novel adaptive self-organized maps based on bidirectional approximate reasoning (ASOMBAR) is proposed to improve the competitive and cooperative process based on the similarity measuring. Unlike the SOM, the proposed ASOMBAR employs the novel fuzzy similarity distance and fuzzy matching criterion to replace the Euclidean distance and original matching criterion, respectively. The fuzzy similarity distance describes the similarity relation more precisely than the Euclidean distance does. The fuzzy matching criterion pays more attention on the large weighted elements and less emphasis on the small weighted elements. Moreover, the new compatible topological neighborhood is also modified basing on the new fuzzy similarity distance and fuzzy matching criterion. Since the ASOMBAR network is self-organizing, the weights of the networks change adaptively according to the input changes. Compared with the well-known growing neural gas (GNG) and SOM, ASOMBAR (when λ  0.5) converges quicker to a smaller distortion error. An information filtering example is used to show the effectiveness of ASOMBAR.     

Detecting the motion of a planar surface by line and surface integrals

A mathematical formulation is presented for detecting the 3D motion of a planar surface from the motion of its perspective image without knowing correspondence of points. The motion is determined explicitly by numerical computation of certain line or surface integrals on the image. The same principle is also used to know the position and orientation of a planar surface fixed in the space by moving the camera or using several appropriately positioned cameras, and no correspondence of points is involved. Some numerical examples are also given.     

Synergistic use of Landsat Multispectral Scanner with GIRAS land-cover data to retrieve impervious surface area for the Potomac River Basin in 1975

For many studies of urbanization, particularly those covering broad spatial extents and thus requiring the perspective afforded by satellite remote sensing, the longest time series of impervious surface area (ISA) initiate with the availability of Landsat Thematic Mapper (TM) data in the mid 1980s. Earlier generation remote sensing data, such as Landsat Multispectral Scanner (MSS) do not generally lead to robust representations of impervious surface area due to poor spectral differentiation between impervious and non-impervious surfaces lacking vegetation. In the 1970s, however, the USGS completed a comprehensive mapping of land cover based on aerial photography called the Geographic Information Retrieval and Analysis System (GIRAS). In this paper we describe a methodology for retrieving ISA estimates through the fusion of GIRAS land cover with historic and contemporary remote sensing data, resulting in a data set that is temporally comparable to more modern data sets (e.g. the National Land Cover Database (NLCD) ISA product). We calculate correlation coefficients between our data set and digitized aerial photography at 4 spatial resolutions, exhibiting a maximum correlation of 0.65 at 120-m and 240-m pixel sizes. Our highest resolution product (30-m) exhibited 94% accuracy and a kappa coefficient of 36% in rural areas, and 90.7% accuracy and a kappa coefficient of 59.6% in suburban areas. Further, we show that trends in urban area through time derived from these data exhibit accelerating rates of development throughout the Potomac River basin 1975-2001, including increased development in riparian buffer zones and on steeper topography. These results are applicable wherever low-resolution vector-based data sets of urban area are available or could be generated for the pre-Landsat TM era.     

Reliability maps for probabilistic guarantees of task motion for robotic manipulators

There are many applications for which reliable and safe robots are desired. For example, assistant robots for disabled or elderly people and surgical robots are required to be safe and reliable to prevent human injury and task failure. However, different levels of safety and reliability are required for different tasks so that understanding the reliability of robots is paramount. Currently, it is possible to guarantee the completion of a task when the robot is fault tolerant and the task remains in the fault-tolerant workspace (FTW). The traditional definition of FTW does not consider different reliabilities for the robotic manipulator's different joints. The aim of this paper is to extend the concept of a FTW to address the reliability of different joints. Such an extension can offer a wider FTW while maintaining the required level of reliability. This is achieved by associating a probability with every part of the workspace to extend the FTW. As a result, reliable fault-tolerant workspaces (RFTWs) are introduced by using the novel concept of conditional reliability maps. Such a RFTW can be used to improve the performance of assistant robots while providing the confidence that the robot remains reliable for completion of its assigned tasks.     

Efficient motion data indexing and retrieval with local similarity measure of motion strings

Widely used in data-driven computer animation, motion capture data exhibits its complexity both spatially and temporally. The indexing and retrieval of motion data is a hard task that is not totally solved. In this paper, we present an efficient motion data indexing and retrieval method based on self-organizing map and Smith–Waterman string similarity metric. Existing motion clips are first used to train a self-organizing map and then indexed by the nodes of the map to get the motion strings. The Smith–Waterman algorithm, a local similarity measure method for string comparison, is used in clustering the motion strings. Then the motion motif of each cluster is extracted for the retrieval of example-based query. As an unsupervised learning approach, our method can cluster motion clips automatically without needing to know their motion types. Experiment results on a dataset of various kinds of motion show that the proposed method not only clusters the motion data accurately but also retrieves appropriate motion data efficiently.     

汶川震后地质灾害遥感信息提取及分布特征分析

以汶川县为研究区,利用遥感、GIS和计算机三维可视化技术,对地质灾害进行了信息提取和解译,共解译滑坡53个,崩塌1 575个,地质灾害面积76.58 km2,并建立了相应的数据库。利用GIS空间分析功能,结合高程、坡度、坡向、地层岩性、地震烈度和水系等因子对研究区地质灾害的空间分布特征进行了分析,并采用层次分析法对研究区地质灾害易发性预测进行了分区研究。研究结果表明,本数据与分析对安置地的重建和地震灾害预测具有很好的参考价值。     

Adaptively imperceptible video watermarking based on the local motion entropy

This paper proposes an adaptively imperceptible video watermarking algorithm using the entropy model for local motion characterization. The algorithm firstly combines Human Visual System (HVS) with the block- matching techniques to get the motion-related information. Then it utilizes the entropy model to statistically analyze above motion-related information to obtain the motion entropy of frame. Successively, this algorithm divides every frame into local regions, and then local motion entropy can be obtained according to the motion-related information in local region. By combining the local motion entropy with the motion entropy of frame, the motion characteristics visual masking is adaptively calculated. Based on the motion characteristics visual masking and the contents of video frames, the maximum strength of watermarking is calculated in every block. Experiments indicate that using entropy to local motion characteristics can significantly improve the watermarking imperceptibility, effectively resist common attacks for video watermarking and consequently achieve higher robustness.