复杂场景下应用成像Ladar的自动目标识别

马超杰; 杨华; 李晓霞; 吴丹; 黄超超

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复杂场景下应用成像Ladar的自动目标识别

信息科学 | 更新时间：2020-08-12

- 复杂场景下应用成像Ladar的自动目标识别
- Implementation of automatic target recognition by imaging Ladar in complex scenes
- 光学精密工程 2009年17卷第7期页码：1714-1721
- 作者机构：
  
  1. 电子工程学院安徽省红外与低温等离子体重点实验室,安徽合肥,230037
  2. 武警指挥学院,天津 300350
- 作者简介：
- 基金信息：
  
  安徽省红外与低温等离子体重点实验室基金资助项目(No.2007A011011F)()
- DOI：
  中图分类号： TN957.52;TP391.44
- 收稿日期：2008-07-25，
  
  修回日期：2008-09-15，
  
  网络出版日期：2009-07-25，
  
  纸质出版日期：2009-07-25
- 稿件说明：
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马超杰, 杨华, 李晓霞, 吴丹, 黄超超. 复杂场景下应用成像Ladar的自动目标识别[J]. 光学精密工程, 2009,17(7): 1714-1721

MA Chao-jie, YANG Hua, LI Xiao-xia, WU Dan, HUANG Chao-chao. Implementation of automatic target recognition by imaging Ladar in complex scenes[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1714-1721
马超杰, 杨华, 李晓霞, 吴丹, 黄超超. 复杂场景下应用成像Ladar的自动目标识别[J]. 光学精密工程, 2009,17(7): 1714-1721 DOI：

MA Chao-jie, YANG Hua, LI Xiao-xia, WU Dan, HUANG Chao-chao. Implementation of automatic target recognition by imaging Ladar in complex scenes[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1714-1721 DOI：

摘要

针对目标部分被树木遮挡时造成的识别困难

提出了基于成像激光雷达(Ladar)的自动目标识别算法。介绍了采用的数据分类、数据拼接、目标姿态调整和基于高度直方图的目标识别等方法的操作步骤。应用图像处理技术对场景数据进行分类

实现地面、目标和树木的分离;利用图像帧之间内在的相关性

采用"由粗到精"的方法求出数据变换矩阵并进行数据拼接;根据目标位置的法线方向进行目标姿态调整得到目标高度直方图。最后

将采集的目标高度直方图同存储的模型高度直方图进行匹配

识别出目标类型。实验结果表明:目标提取部分占目标总面积的90%以上

对目标的识别率高于99%

基本满足自动目标识别算法的稳定可靠、识别率高、抗干扰能力强等要求。

Abstract

A novel Automatic Target Recognition(ATR) algorithm based on imaging Laser Radar(Ladar) is proposed to solve the target recognition difficulty caused by a tree sheltering

and the algorithms such as data classification

data mosaic

target pose adjustment and target recognition based on a height histogram are studied. The scene data are classified into several parts (ground

targets and trees) by using an image processing technique

and the translation matrixes among different views are calculated from coarse to fine according to their inherent attributions. Then

the data from different views are integrated together. According to its normal directions

each target pose is adjusted to the top view

and its height histogram can be calculated. Finally

the target is recognized by matching the target height histogram with the saved model height histograms. Experimental results indicate that the extracted target part is more than 90% of the whole target area and the recognition ratio is more than 99%

which satisfies the ATR requirements in stabilization

higher recognition ratios and strong anti-jamming.

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Keywords

references

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