Multisource remote sensing data fusion using fuzzy self-organization mapping network and modified Dempster-Shafer evidential reasoning method of classification

It is always advisable to apply filtering only on corrupted pixels of images leaving untouched the uncorrupted ones to preserve image features and to avoid blurring effects. We present an algorithm which can detect the corrupted pixels in texture images. It uses a fuzzy Kohonen clustering network that integrates with the fuzzy c-means (FCM) model utilizing the updating strategies of the first and the learning rate of the second.

A fuzzy Kohonen (1989) clustering network (FKCN) algorithm is proposed for image segmentation. Large image sizes are in general required for various types of imaging applications. For a large amount of data, computation of the FKCN algorithm for iterative operation will take a long time. Therefore, an attempt has been made to make the FKCN algorithm realistically useful for image segmentation. The proposed FKCN algorithm keeps its advantages, but instead of using the image space, a gray level function of the image was used for all imaging data. A new algorithm was developed which provided good results in the short time taken for image segmentation.

the difficulties of wireless sensor data fusion is to switch the sensor cluster state, namely the problem at some point the direction to which the sensor data fusion. First of all, by the acquisition of knowledge in rough set theory, the typical clustering distribution of multiple sensors can be fused as sample space form decision table data fusion distribution, then according to the method of knowledge reduction in rough sets, attributes and sample data redundancy to delete. And then the clustering analysis and fuzzy Kohonen clustering network, and finally the formation of distribution rules of sensor data fusion. The experimental results show that the model has good classification efficiency, can realizes the clustering distribution of judgment.

The clustering of high-dimensional data presents a critical computational problem. Therefore, it is convenient to arrange the cluster centres on a grid with a small dimensional space that reduces computational cost and can be easily visualized. Moreover, in real applications there is no sharp boundary between classes, real datasets are naturally defined in a fuzzy context. Therefore, fuzzy clustering fits better for complex real datasets to determine the best distribution. Self-organizing map (SOM) technique is appropriate for clustering and vector quantization of high-dimensional data. In this paper we present a new fuzzy learning approach called FMIG (fuzzy multilevel interior growing self-organizing maps). The proposed algorithm is a fuzzy version of MIGSOM (multilevel interior growing self-organizing maps). The main contribution of FMIG is to define a fuzzy process of data mapping and to take into account the fuzzy aspect of high-dimensional real datasets. This new algorithm is able to auto-organize the map accordingly to the fuzzy training property of the nodes. In the second step, the introduced scheme for FMIG is clustered by means of fuzzy C-means (FCM) to discover the interior class distribution of the learned data. The validation of FCM partitions is directed by applying six validity indexes. Superiority of the new method is demonstrated by comparing it with crisp MIGSOM, GSOM (growing SOM) and FKCN (fuzzy Kohonen clustering network) techniques. Thus, our new method shows improvement in term of quantization error, topology preservation and clustering ability.

Information on the spatial variation of soil fertility attributes is an essential input in precision agriculture for soil management decisions. In this study, soil fertility was evaluated through the spatial distribution of property maps and subsequent integration into fertility classes, as a fundamental basis for the implementation of fertilization plans and amendments adjusted to crop requirements. For the evaluation of fertility, a systematic surface sampling was carried out at 70 sites in the "Agronomy" production field of Romulo Gallegos University, The Castrero sector, Roscio municipality, Guárico state, Venezuela. Ten soil attributes were analyzed: pH, electrical conductivity, organic matter, phosphorus, potassium, calcium, magnesium, and the relative amounts of sand, silt, and clay. Soil property maps were produced by geostatistical analysis and ordinary kriging interpolation, and artificial intelligence techniques based on an artificial neural network classification system, with the FKCN (fuzzy Kohonen clustering network) algorithm, were applied to generate soil fertility classes. The reliability of the maps for each variable was obtained by cross-validation with a reliability of more than 90%. The integration of the maps produced a map composed of five categories. The final soil class model presented a reliability equivalent to 86%, indicating a high degree of homogeneity within the soil classes obtained. This approach overcomes the limitations of traditional methods by integrating multiple variables into a coherent model and is capable of generating information that can be used as a basis for the establishment of experimental plots for research purposes and the specific management of nutrients present in the soil resource of the area under consideration.

Data fusion has been widely used in various fields of automation. This paper describes a multisensor integration system: range and intensity image processing system, which can be used for object recognition and classification. In the data fusion processing, a new method called generalized evidence inference method is used by the system. The method presented here unifies both Bayesian theory and Dempster-Shafer's evidential reasoning (DSER) for the combination of information from diversified sources, and overcomes the disadvantages of both approaches. At the same time, we adopt these three approaches: the Bayesian theory, the DSER, and the unified approach to fuse the reports in the system for object recognition and classification, the results are compared and analyzed.

The existing building surface data fusion algorithms do not extract the segmented data features, resulting in inaccurate fusion results. In heterogeneous environment, a Clustering Fusion Algorithm Based on mutual information and fractal dimension is proposed. The regression coefficient is used to express the sequence, and the data feature representation and data dimension reduction are realized. The dynamic data series are processed by similarity measure function method. For the long dynamic data series, the piecewise aggregation approximation method is used to segment the data and then extract the features. Through the incremental clustering processing data based on fractal dimension clustering algorithm, the research of data fusion algorithm is realized. The experimental results show that the accuracy of building surface data fusion is greatly improved by using the dynamic data fusion algorithm, the highest is 0.98, the sum of square error is reduced, and the lowest is only 90.44.

Data Fusion for ITS: Techniques and Research Needs

Sensor networks are attracting attention in several fields. However, the feasibility of such networks faces several challenges, two of which are data fusion and error reduction. This paper presents data fusion and high level error correction algorithms for sensor networks. These algorithms are scalable and general, and thus can be applied to networks of any size using any type of sensors. The data fusion procedure developed results in significant reduction of data sent without reducing the amount of information provided. This allows for real-time remote monitoring of information across low bandwidth connections such as the Internet. The high level error reduction is accomplished using a probability matrix and results in a significant amount of error elimination. A sensor network capable of tracking object motion is constructed to evaluate the performance of the two algorithms. The experimental results obtained confirmed the theory presented.

Microelectronic technology and communication technology are developed in deep manner; the computing mode has been transferred from traditional computer-centered to human centered pervasive. So, the concept of Internet of things (IoT) is gradually put forward, which allows people to access information about their surroundings on demand through different terminals. The library is the major public space for human to read and learn. How to provide a more comfortable library environment to better meet people’s learning requirements is a place where the Internet of things plays its role. The purpose of this paper is to solve the difference between the data fusion of library environment and the data fusion of other environments by the method of data fusion oriented to library. This paper presents a general technical framework of situational awareness for smart library system which includes a data fusion middleware. It can process data and inform the upper module of the changed library environment after deploying the smart library system in a library, including data collection and processing, how to judge whether events are triggered, how the system reacts, and the acquisition and update of user preferences. This paper presents a situational awareness recommendation method based on an effective data fusion model and algorithm for library after conducting experimental in service of library, which give more accurate of book recommendation than traditional method and good learning service environment of library for readers.

JAID: An algorithm for data fusion and jamming avoidance on distributed sensor networks

Advances in information technology enable a capability to gather large amounts of raw data from all parts of our society to a degree of efficiency that can actually encumber surveillance missions. Today one can create automated networks of sensors to gather enormous volumes of data, but we do not have the human capacity to sort through all this raw data. Similar problems exist in the area of machinery condition or health monitoring - another surveillance problem. By automating data collection, processing, fusion and interpretation, one can bring the most relevant and timely information to human analysts, planners, and responders. Key technologies that enable this transformation from data to knowledge are distributed hardware and software architectures, intelligent sensors, data fusion and reasoning algorithms, and open system architectures for information exchange. Distributed hardware and software structures partition complex systems into a collection of interconnected subsystems. Intelligent sensors enable this conversion of data to information by processing massive amounts of data at the subsystem and higher levels to extract contextually relevant information. The transformation from raw sensor data to useful information requires the application of subsystem-specific signal processing and feature extraction algorithms, data fusion, and classification algorithms to combine data and features from commensurate and non-commensurate sensors or information sources. Emerging standards in open system architectures for condition based maintenance apply equally to surveillance systems and condition monitoring systems. Examples from fielded system health monitoring applications are presented along with their parallels to surveillance systems with application to homeland defense.

In land-use classification of hyperspectral remote sensing (RS) images, traditional classification methods often experience large amount of datasets and low efficiency. To solve these problems, a fast machine-learning method, the extreme learning machine (ELM) algorithm, was introduced. However, basic use of the ELM usually encounters problems of unstable classification results and low classification accuracy. Hence, in this paper, optimization methods for ELM-based RS image classification were mainly discussed and applied to solve the bottleneck problems. From the three perspectives of ensemble learning, making full use of image texture features, and deep learning, three classification optimization methods have been designed and implemented. The results show that: 1) To some extent, all the three methods can achieve a balance between classification accuracy and efficiency, i.e., they can maintain the advantage of ELM algorithm in classification efficiency and speed while have better classification accuracy; 2) The image texture feature optimization method (LBP-KELM) solves the problem of unsatisfactory classification results experienced by the ensemble learning optimization method (Ensemble-ELM) and further improves classification accuracy. However, the classification results are sensitive to the type of dataset; and 3) Fortunately, the optimization method combined with deep learning (CNN-ELM) can meet the application needs of multiple datasets. Furthermore, it can also further improve classification accuracy.

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES