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摘要: 近年来，随着光谱成像技术的发展，机载成像光谱仪在海域军事目标的侦察中得到了新的应用。基于此，本文首先从高光谱成像仪的基本原理及特性出发，介绍了高光谱成像仪在海洋军事目标探测方面的应用现状。其次，分别从水面目标探测和水下目标探测两方面综合分析了光谱成像技术在海域目标探测中的应用。对于海面目标探测，多项关键技术获得突破，但当前算法仍然难以解决实时性问题；对于水下目标探测，本文主要以水下潜艇探测为例探讨了利用高光谱成像仪对水下目标进行探测的关键技术及相关可行性方案。分析可知，光谱成像技术用于海洋军事探测从技术上具有可行性且前景广阔，但仍需解决相关算法的效率及精度等关键问题，这对推动光谱成像技术在海域目标探测中的应用具有重要意义。Abstract: In recent years, with the development of spectral imaging technology, airborne imaging spectrometer has been applied in the military target reconnaissance. Firstly, based on the basic principle and characteristics of hyperspectral imager, this paper introduces the application of hyperspectral imager in marine military target detection. Secondly, the application of spectral imaging technology in maritime target detection is analyzed from both the water surface target detection and the underwater target detection. For surface target detection, many key technologies have made breakthroughs, while the current algorithms are still difficult to solve the real-time problems; for underwater target detection, this paper takes the underwater detection as an example to discuss the key technology and feasibility of the underwater target detection based on hyperspectral imager. The analysis shows that the application of spectral imaging technology to military detection is technically feasible and promising. However, the key issues such as the efficiency and accuracy of the algorithm still need to be solved, which is of great significance for the application of spectral imaging in maritime target detection.
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Key words:
- spectral imaging /
- target detection /
- underwater target /
- hyperspectral
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图 2 AVIRIS系统光机结构及ER-2机载平台
Figure 2. Optical-mechanical of AVIRIS and ER-2 airborne platform
图 4 HSI系统及在“捕食者”无人机安装位置
Figure 4. HSI system and the installation site in Predator drone
图 5 HSI地面处理系统及获得的高光谱数据
Figure 5. HSI ground processing system and the obtained hyperspectral data
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[1] 郑玉权, 王慧, 王一凡.星载高光谱成像仪光学系统的选择与设计[J].光学 精密工程, 2009, 17(11):2630-2631. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm200911004&dbname=CJFD&dbcode=CJFQZHENG Y Q, WANG H, WANG Y F. Selection and design of optical systems for spaceborne hyperspectral imagers[J]. Opt. Precision Eng., 2009, 17(11):2630-2631.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm200911004&dbname=CJFD&dbcode=CJFQ [2] LABAW C. Airborne imaging spectrometer:an advanced concept instrument[J]. SPIE, 1983, 430:68-73. http://adsabs.harvard.edu/abs/1983SPIE..430...68L [3] GREEN R O, EASTWOOD M L, SARTURE C M, et al.. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer(AVIRIS)[J]. Remote Sensing of Environment, 1998, 65(3):227-248. doi: 10.1016/S0034-4257(98)00064-9 [4] 李越强, 李庶中, 贾宇.光谱成像技术在海上目标探测识别中的应用探讨[J].光学与光电技术, 2015, 13(1):79-86. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxgd201501020&dbname=CJFD&dbcode=CJFQLI Y Q, LI S Z, JIA Y. Application of spectral imaging technology in detection and recognition of marine targets[J]. Optics & Optoelectronic Technology, 2015, 13(1):79-86.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxgd201501020&dbname=CJFD&dbcode=CJFQ [5] 张达, 郑玉权.高光谱遥感的发展与应用[J].光学与光电技术, 2013, 11(3):67-73. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxgd201303017&dbname=CJFD&dbcode=CJFQZHANG D, ZHENG Y Q. Hyperspectral remote sensing and its development and application review[J]. Optics & Optoelectronic Technology, 2013, 11(3):67-73.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxgd201303017&dbname=CJFD&dbcode=CJFQ [6] 高国龙.高光谱成像仪寻求军事用途[J].红外, 2004, 4:48. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=hwai200404015&dbname=CJFD&dbcode=CJFQGAO G L. Hyperspectral imager for military use[J]. Infrared, 2004, 4:48.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=hwai200404015&dbname=CJFD&dbcode=CJFQ [7] BORK E W, SU J G. Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation:a meta analysis[J]. Remote Sensing of Environment, 2007, 111(1):11-24. doi: 10.1016/j.rse.2007.03.011 [8] 麻永平, 张炜, 刘东旭.高光谱侦察技术特点及其对地面军事目标威胁分析[J].上海航天, 2012, 29(1):37-40. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=shht201201009&dbname=CJFD&dbcode=CJFQMA Y P, ZHANG W, LIU X D. Characteristics of hyperspectral reconnaissance and threat to ground military targets[J]. Aerospace Shanghai, 2012, 29(1):37-40.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=shht201201009&dbname=CJFD&dbcode=CJFQ [9] 曹佃生, 石振华, 林冠宇.机载海洋改进型Dyson高光谱成像仪的研制[J].光学 精密工程, 2017, 25(6):1404-1405. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm201706001&dbname=CJFD&dbcode=CJFQCAO D SH, SHI ZH H, LIN G Y. Development of airborne ocean modified Dyson hyperspectral imager[J]. Opt. Precision Eng., 2017, 25(6):1404-1405.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm201706001&dbname=CJFD&dbcode=CJFQ [10] HUANG Z W, SHI Z W, QIN Z. Convex relaxation based sparse algorithm for hyperspectral target detection[J]. Optik, 2013, 124(24):6594-6598. doi: 10.1016/j.ijleo.2013.05.091 [11] 杜山山, 李姝颖, 曾朝阳.背景不确定性对高光谱异常目标探测的影响[J].解放军理工大学学报(自然科学版), 2016, 17(6):598-604. http://www.cqvip.com/QK/90576X/201606/670888405.htmlDU SH SH, LI SH Y, ZENG CH Y. Influence of background uncertain target detection[J]. J. PLA University of Science and Technology Natural Science Edition, 2016, 17(6):598-604.(in Chinese) http://www.cqvip.com/QK/90576X/201606/670888405.html [12] 赵志勇, 吕绪良, 刘凯龙, 等.基于高光谱的目标探测方法分析[J].光电技术应用, 2010, 25(3):3-5. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gdyg201003004&dbname=CJFD&dbcode=CJFQZHAO ZH Y, LV X L, LIU K L, et al.. Analysis of target detecting methods based on hyper-spectrum[J]. Electro-optic Technology Application, 2010, 25(3):3-5.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gdyg201003004&dbname=CJFD&dbcode=CJFQ [13] KWON H, NASRABADI N M. Kernel RX-algorithm:A nonlinear anomaly detector for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2):388-397. doi: 10.1109/TGRS.2004.841487 [14] MOLERO J M, GARZON E M, GARCIA I, et al.. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):801-814. doi: 10.1109/JSTARS.2013.2238609 [15] ROSSI A, ACITO N, DIANI M, et al.. RX architectures for real-time anomaly detection in hyperspectral images[J]. J. Real-Time Image Processing, 2014, 9(3):503-517. doi: 10.1007/s11554-012-0292-3 [16] GUO W J, ZENG X R, ZHAO B W, et al.. Multi-DSP parallel processing technique of hyperspectral RX anomaly detection[J]. Spectroscopy and Spectral Analysis, 2014, 34(5):1383-1387. http://www.ncbi.nlm.nih.gov/pubmed/25095443 [17] KHAZAI S, MOJARADI B. A modified kernel-RX algorithm for anomaly detection in hyperspectral images[J]. Arabian Journal of Geosciences, 2015, 8(3):1487-1495. doi: 10.1007/s12517-013-1218-5 [18] 杜小平, 刘明, 夏鲁瑞, 等.基于光谱角累加的高光谱图像异常检测算法[J].中国光学, 2013, 6(3):327-328. http://www.chineseoptics.net.cn/CN/abstract/abstract8941.shtmlDU X P, LIU M, XIA L R, et al.. Anomaly detection algorithm for hyperspectral imagery based on summation of spectral angles[J]. Chinese Optics, 2013, 6(3):327-328.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract8941.shtml [19] KHAZAI S, HOMAYOUNI S, SAFARI A, et al.. Anomaly detection in hyperspectral images based on an adaptive support vector method[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4):646-650. doi: 10.1109/LGRS.2010.2098842 [20] ZHANG L F, ZHANG L P, TAO D C, et al.. Hyperspectral remote sensing image subpixel target detection based on supervised metric learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8):4955-4965. doi: 10.1109/TGRS.2013.2286195 [21] ZHANG B, YANG W, GAO L R, et al.. Real-time target detection in hyperspectral images based on spatial-spectral information extraction[J]. Eurasip Journal on Advances in Signal Processing, 2012, 142. doi: 10.1186/1687-6180-2012-142 [22] 徐芳, 刘晶红, 曾冬冬, 等.基于视觉显著性的无监督海面舰船检测与识别[J].光学 精密工程, 2017, 25(5):1300-1310. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm201705023&dbname=CJFD&dbcode=CJFQXU F, LIU J H, ZENG D D, et al.. Detection and identification of unsupervised ships and warships on sea surface based on visual saliency[J]. Opt. Precision Eng., 2017, 25(5):1300-1310.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=gxjm201705023&dbname=CJFD&dbcode=CJFQ [23] CHEIN I C, SHAO-SHAN C. Anomaly detection and classification for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(6):1314-1325. doi: 10.1109/TGRS.2002.800280 [24] ZHAO R, DU B, ZHANG L P. A robust nonlinear hyperspectral anomaly detection approach[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4):1227-1234. doi: 10.1109/JSTARS.4609443 [25] 张旭升, 郭亮, 胡日查, 等.红外探测中潜艇冷热尾流的传热传质特性[J].光学 精密工程, 2017, 25(1):107-114. http://www.eope.net/EN/Y2017/V25/I1/107ZHANG X SH, GUO L, HU R CH, et al.. Heat and mass transfer characteristic of submarine cold-thermal wake in the infrared detection[J]. Opt. Precision Eng., 2017, 25(1):107-114.(in Chinese) http://www.eope.net/EN/Y2017/V25/I1/107 [26] DU B, ZHANG L P. Random-selection-based anomaly detector for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5):1578-1589. doi: 10.1109/TGRS.2010.2081677 [27] HUANG Z W, SHI Z W, QIN Z. Convex relaxation based sparse algorithm for hyperspectral target detection[J]. Optik, 2013, 124(24):6594-6598. doi: 10.1016/j.ijleo.2013.05.091 -
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