基于Landsat 8的南极内陆考察车辙印自动提取
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摘要
分析了内陆考察车辙印的Landsat 8卫星影像特征,利用目视解译以及统计量分析车辙印与背景雪面的差异,得出近红外和短波红外波段是车辙印自动提取的优选波段,高斯增强方法为提升车辙印与背景之间的对比度的优选方案;同时运用动感模糊、canny边缘提取算子、膨胀腐蚀算子和噪声线段去除技术,实现车辙印在不同环境下的高精度自动提取.通过对中国第33次南极考察期间的内陆考察车辙印进行自动提取试验验证,此算法直接实现54%路线上车辙印的自动提取,32%的路线略需人工干预,仅14%的路线难以实现自动识别.另外,本文结合雪积累率、雪粒径大小、冰流速以及地形数据分别分析了气候、冰雪物理特性、冰川动力特征和地形特征对车辙印识别的影响.
Increasing human activities in the Antarctica have left many human behavioral traces on the continent,records left for the development of human exploration.Remote sensing is important to record such behavior traces.Automatic extraction of such weak information makes possible technical break-throughs in weak information extraction by remote sensing.Rapid tracking and recording of human activity/information in Antarctica has both technical and application values.We analyzed characteristics of Landsat 8 satellite images of rutting in inland investigation,and differences in rutting and background snow surface by visual interpretation and statistical analysis.Near-infrared and short-wave infrared bands were found to be optimal bands for automatic extraction of rutting.Gaussian enhancement was optimal scheme to improve contrast between rutting and background.Motion blur,canny edge extraction operator,expansion corrosion algorithm and noise line segment removal technology were used to achieve high-precision automatic extraction of ruts in different environments.By automatic rut extraction test of the 33 rd Chinese Antarctic expedition,this algorithm directly realized automatic rut extraction on 54% routes,with 32% routes slightly needing manual intervention,14%routes were hard for automatic recognition.Snow accumulation rate,snow grain size,ice velocity and topographic data were combined to analyze the influence of climate,ice and snow physical characteristics,glacier dynamic characteristics and topographic characteristics on rut recognition.
Increasing human activities in the Antarctica have left many human behavioral traces on the continent,records left for the development of human exploration.Remote sensing is important to record such behavior traces.Automatic extraction of such weak information makes possible technical break-throughs in weak information extraction by remote sensing.Rapid tracking and recording of human activity/information in Antarctica has both technical and application values.We analyzed characteristics of Landsat 8 satellite images of rutting in inland investigation,and differences in rutting and background snow surface by visual interpretation and statistical analysis.Near-infrared and short-wave infrared bands were found to be optimal bands for automatic extraction of rutting.Gaussian enhancement was optimal scheme to improve contrast between rutting and background.Motion blur,canny edge extraction operator,expansion corrosion algorithm and noise line segment removal technology were used to achieve high-precision automatic extraction of ruts in different environments.By automatic rut extraction test of the 33 rd Chinese Antarctic expedition,this algorithm directly realized automatic rut extraction on 54% routes,with 32% routes slightly needing manual intervention,14%routes were hard for automatic recognition.Snow accumulation rate,snow grain size,ice velocity and topographic data were combined to analyze the influence of climate,ice and snow physical characteristics,glacier dynamic characteristics and topographic characteristics on rut recognition.
引文
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[3]HEIPKE C,STEGER C T,MULTHAMMER R.Hierarchical approach to automatic road extraction from aerial imagery[C]∥Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision II:International Society for Optics and Photonics.1995:222
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[5]HUI F,LI X,ZHAO T,et al.Semi-automatic mapping of tidal cracks in the fast ice region near Zhongshan station in East Antarctica using Landsat-8 OLI imagery[J].Remote Sensing,2016,8(3):242
[6]MEER P,GEORGESCU B.Edge detection with embedded confidence[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2001,23(12):1351
[7]ABO-ZAHHAD M,GHARIEB R R,AHMED S M,et al.Edge detection with a preprocessing approach[J].Journal of Signal and Information Processing,2014,5(4):123
[8]HEATH M,SARKAR S,SANOCKI T,et al.Comparison of edge detectors:a methodology and initial study[J].Computer Vision and Image Understanding,1998,69(1):38
[9]DING J J,LEE P X.Fast morphology algorithm with parallel processing structures[C]∥Audio,Language and Image Processing(ICALIP),2014 International Conference on:IEEE,2014:324
[10]MOREAUD M,ITTHIRAD F.Fast algorithm for dilation and erosion using arbitrary flat structuring element:Improvement of urbach and Wilkinson's algorithm to GPU computing[C]∥Multimedia Computing and Systems(ICMCS),2014International Conference on:IEEE,2014:289
[11]CLIENTI C,BILODEAU M,BEUCHER S.An efficient hardware architecture without line memories for morphological image processing[C]∥International Conference on Advanced Concepts for Intelligent Vision Systems:Springer,2008:147-156
[12]MALAMAS E N,MALAMOS A G,VARVARIGOUT A.Fast implementation of binary morphological operations on hardware-efficient systolic architectures[J].Journal of VLSI Signal Processing Systems for Signal,Image and Video Technology,2000,25(1):79
[13]HEDBERG H,DOKLADAL P,OWALL V.Binary morphology with spatially variant structuring elements:Algorithm and architecture[J].IEEE Transactions on Image Processing,2009,18(3):562
[14]邓仕超,黄寅.二值图像膨胀腐蚀的快速算法[J].计算机工程与应用,2017,53(5):207
[15]杨琨,曾立波,王殿成.数学形态学腐蚀膨胀运算的快速算法[J].计算机工程与应用,2005,41(34):54
[16]RIGNOT E,MOUGINOT J,SCHEUCHL B.Ice flow of the Antarctic ice sheet[J].Science,2011,333(6048):1427
[17]MOUGINOT J,SCHEUCHL B,RIGNOT E.Mapping of ice motion in Antarctica using synthetic-aperture radar data[J].Remote Sensing,2012,4(9):2753
[18]ARTHERN R J,WINEBRENNER D P,VAUGHAND G.Antarctic snow accumulation mapped using polarization of 4.3-cm wavelength microwave emission[J].Journal of Geophysical Research:Atmospheres,2006,111(D6)
[19]FRETWELL P,PRITCHARD H D,VAUGHAN D G,et al.Bedmap2:improved ice bed,surface and thickness datasets for Antarctica[J].The Crgosphere,2013,7(1):375
[20]SCAMBOS T,HARAN T,FAHNESTOCK M,et al.MODIS-based mosaic of Antarctica(MOA)data sets:continent-wide surface morphology and snow grain size[J].Remote Sensing of Environment,2007,111(2/3):242