A neural system for automatic target learning and recognition applied to bare and camouflaged SAR targets

被引：11

作者：

Bernardon, AM ^{[1
]}

Carrick, JE ^{[1
]}

机构：

[1] MIT,LINCOLN LAB,MACHINE INTELLIGENCE TECHNOL GRP,LEXINGTON,MA 02173

来源：

NEURAL NETWORKS | 1995年 / 8卷 / 7-8期

关键词：

target detection; target recognition; object learning; object recognition; SAR; ISAR; neural networks;

D O I：

10.1016/0893-6080(95)00063-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a neural based approach to target learning and recognition in synthetic-aperture radar imagery. Targets consist of a variety of camouflaged and uncamouflaged military vehicles taken at different radar view and depression angles in both spotlight and stripmap radar collection modes. Results from a variety of recognition experiments are reported.

引用

页码：1103 / 1108

页数：6

共 6 条

[1] ART 2-A - AN ADAPTIVE RESONANCE ALGORITHM FOR RAPID CATEGORY LEARNING AND RECOGNITION
CARPENTER, GA
GROSSBERG, S
ROSEN, DB
[J]. NEURAL NETWORKS, 1991, 4 (04) : 493 - 504
[2] Cutrona L., 1970, RADAR HDB
[3] DUDGEON DE, 1994, P SOC PHOTO-OPT INS, V2230, P356, DOI 10.1117/12.177192
[4] NOVAL KM, 1993, LINCOLN LABORATORY J, V5, P11
[5] Waxman A. M., 1993, Lincoln Laboratory Journal, V6, P77
[6] Neural processing of targets in visible multispectral IR and SAR imagery
Waxman, AM
Seibert, MC
Gove, A
Fay, DA
Bernardon, AM
Lazott, C
Steele, WR
Cunningham, RK
[J]. NEURAL NETWORKS, 1995, 8 (7-8) : 1029 - 1051

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