A moving ISAR-object recognition using pi-sigma neural networks based on histogram of oriented gradient of edge

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2021-08-12 DOI:10.1080/19479832.2021.1953620

Asma Elyounsi, H. Tlijani, M. Bouhlel

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引用次数: 3

Abstract

ABSTRACT Detection and classification with traditional neural networks methods such as multilayer perceptron (MLP), feed forward network and back propagation neural networks show several drawbacks including the rate of convergence and the incapacity facing the problems of size of the image especially for radar images. As a result, these methods are being replaced by other evolutional classification methods such as Higher Order Neural Networks (HONN) (Functional Link Artificial Neural Network (FLANN), Pi Sigma Neural Network (PSNN), Neural Network Product Unit (PUNN) and Neural Network of the Higher Order Processing Unit. So, in this paper, we address radar object detection and classification problems with a new strategy by using PSNN and a new proposed method HOGE for edges features extraction based on morphological operators and histogram of oriented gradient. Thus, in order to recognise radar object, we extract HOG features of the object region and classify our target with PSNN. The HOGE features vector is used as input of pi-sigma NN. The proposed method was tested and confirmed based on experiments through the use of 2D and 3D ISAR images.

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基于边缘方向梯度直方图的pi-sigma神经网络运动isar目标识别

传统的神经网络方法，如多层感知器（MLP）、前馈网络和反向传播神经网络，在检测和分类方面存在着一些缺点，包括收敛速度和无法解决图像大小问题，尤其是对于雷达图像。结果，这些方法正被其他进化分类方法所取代，例如高阶神经网络（HONN）（功能链接人工神经网络（FLANN））、Pi-Sigma神经网络（PSNN）、神经网络乘积单元（PUNN）和高阶处理单元的神经网络。因此，在本文中，我们使用PSNN的一种新策略和一种新的基于形态学算子和定向梯度直方图的边缘特征提取方法HOGE来解决雷达目标检测和分类问题。因此，为了识别雷达目标，我们提取目标区域的HOG特征，并使用PSNN对目标进行分类。HOGE特征向量被用作pi西格玛神经网络的输入。通过使用二维和三维ISAR图像，在实验的基础上对所提出的方法进行了测试和验证。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).