Spectrum spatial structure characteristic analysis of remote sensing alteration information and interference factors

Zi-an Yang , Sheng-lin Peng , Gu-chang Zhu , Fei-fei Shi , Lin Zou , Yuan-fei Zhang

Journal of Central South University ›› 2009, Vol. 16 ›› Issue (4) : 647 -652.

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Journal of Central South University ›› 2009, Vol. 16 ›› Issue (4) : 647 -652. DOI: 10.1007/s11771-009-0107-2
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Spectrum spatial structure characteristic analysis of remote sensing alteration information and interference factors

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Abstract

Based on the statistical characteristics of remote sensing data, the spatial geometric structure characteristics of spectral data and distribution of background, interference and alteration information in characteristic space were researched through the analysis of two-dimensional and three-dimensional scatter diagrams. The results indicate that the hyper-space of remote sensing multi-data aggregation belongs to low-dimensional geometric structure, i.e. hyperplane form, and anomalous point groups including alteration information usually dissociate out of hyperplane. Scatter diagrams of remote sensing data band are mainly presented as two distribution forms of single-ellipse and dual-ellipse. Clarifying the relations of three objects of background, disturbance and alteration information in remote sensing images provides an important technical thought and guidance for accurately detecting and extracting remote sensing alteration information.

Keywords

remote sensing data / alteration information / background / interference factor / spectrum spatial structure / scatter diagram

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Zi-an Yang, Sheng-lin Peng, Gu-chang Zhu, Fei-fei Shi, Lin Zou, Yuan-fei Zhang. Spectrum spatial structure characteristic analysis of remote sensing alteration information and interference factors. Journal of Central South University, 2009, 16(4): 647-652 DOI:10.1007/s11771-009-0107-2

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References

[1]

YaoF.-l., SunF.-yue.Course of ore deposit [M], 2006, Beijing, Geological Publishing House

[2]

F.-j., XiangL.-x., FanJ.-z., RuanJ.-w., WuC.-w., DuanZ.. Application study of remotely sensed alteration information extraction [J]. Xinjiang Geology, 2004, 22(4): 435-437

[3]

ZhangY.-j., YangJ.-m., ChenW.. A study of the method for extraction of alteration anomalies from the ETM+(TM) data and its application: Geologic basis and spectral precondition [J]. Remote Sensing for Land and Resources, 2002, 54(4): 30-37

[4]

ZouL., YangZ.-a., ZhuG.-c., WuD.-w., XiangA.-qin.. New method of alteration information extraction from multi-spectrum remote sensing data [J]. Geology and Prospecting, 2006, 42(6): 71-76

[5]

GeolP. K., ParsherS. O., PatelR. M., LandryJ. A., BonnellR. B., ViauA. A.. Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn [J]. Computers and Electronics in Agriculture, 2003, 39(12): 67-93

[6]

RaptisV. S., VaughanR. A., WrightG. G.. The effect of scaling on land cover classification from satellite data [J]. Computers and Geosciences, 2003, 29(6): 705-714

[7]

NarayananR. M., DesettyM. K., ReichenbachS. E.. Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy [J]. International Journal of Remote Sensing, 2002, 23(3): 537-553

[8]

ZhangY.-f., WuD.-w., ZhuG.-c., YangZ.-an.. The problems of background and interference in remote sensing alteration information detection [J]. Remote Sensing for Land and Resources, 2008, 84(2): 22-26

[9]

ZhengX.-r., PanJ.-j., LinH., GuM.. Study on the spectral reflectance of four kinds of the mixed land cover types [J]. Journal of Nanjing Agricultural University, 2004, 27(4): 55-59

[10]

LiZ.-y., YuW.-x., KuangG.-y., WuH.. The research of anomaly detection based on high-dimensional geometric feature in hyperspectral imagery [J]. Remote Sensing Technology and Application, 2003, 18(6): 379-383

[11]

RanjbarH., HonarmandM., MoezifarZ.. Application of the Crosta technique for porphyry copper alteration mapping, using ETM+ data in the southern part of the Iranian volcanic sedimentary belt [J]. Journal of Asian Earth Sciences, 2004, 24(2): 237-243

[12]

JunchangJ., KolaczykE. D., SucharitaG.. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing [J]. Remote Sensing of Environment, 2003, 84(2): 550-560

[13]

TakeuchiW., TamuraM., YasuokaY.. Estimation of methane mission from West Siberian wet land by scaling technique between NOAA AVHRR and SPOT HRV [J]. Remote Sensing of Environment, 2003, 85(1): 21-29

[14]

SunR., LiuC.-m., LiX.-wen.. Evapotranspiration estimation in the Yellow River Basin, China, using integrated NDVI data [J]. International Journal of Remote Sensing, 2004, 25(13): 2523-2534

[15]

ChristopherS.. The landsat ETM+ spectral mixing space [J]. Remote Sensing of Environment, 2004, 93(1/2): 1-17

[16]

MedlynB. E.. Carbon balance of coniferous forests growing in contrasting climates: Model-based analysis [J]. Agricultural and Forest Meteorology, 2005, 97(2): 131-138

[17]

MutangaO.. Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain [J]. International Journal of Remote Sensing, 2005, 26(3): 1093-1108

[18]

PanY. D.. Importance of foliar nitrogen concentration to predict forest productivity in the Mid-Atlantic region [J]. Forest Science, 2004, 50(4): 279-289

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