Research article

Identifying Unlawful Constructions in Cultural Relic Sites Based on Subpixel Mapping—a Case Study in Mangshan Tombs, China

  • Received: 14 February 2017 Accepted: 20 June 2017 Published: 29 June 2017
  • Monitoring unlawful constructions in cultural relic sites is difficult in remote and unpopulated areas. This paper aims at facilitating cultural relic protection surveys using remote sensing. High-resolution remote sensing images are better alternatives to field visits for locating unlawful buildings. However, these buildings are usually hidden by vast wildness around the cultural relics, which makes the use of high-resolution imagery costly and inefficient. The main purpose of this research is to develop an approach to subpixel building identification from moderate resolution images, such as Landsat 8 OLI with reasonable accuracy based on the mixture-tuned match filtering (MTMF) partial unmixing method. With this method, pixels with high MF scores and low MT scores were identified as candidate locations of possible unlawful buildings. A case study in the Mangshan Tombs, China demonstrated that this method had a better accuracy for identifying constructions than the commonly used fully-constrained linear unmixing model.

    Citation: Yaping Xu, Lei Wang, Chengliang Liu, Cuiling Liu. Identifying Unlawful Constructions in Cultural Relic Sites Based on Subpixel Mapping—a Case Study in Mangshan Tombs, China[J]. AIMS Geosciences, 2017, 3(2): 268-283. doi: 10.3934/geosci.2017.2.268

    Related Papers:

  • Monitoring unlawful constructions in cultural relic sites is difficult in remote and unpopulated areas. This paper aims at facilitating cultural relic protection surveys using remote sensing. High-resolution remote sensing images are better alternatives to field visits for locating unlawful buildings. However, these buildings are usually hidden by vast wildness around the cultural relics, which makes the use of high-resolution imagery costly and inefficient. The main purpose of this research is to develop an approach to subpixel building identification from moderate resolution images, such as Landsat 8 OLI with reasonable accuracy based on the mixture-tuned match filtering (MTMF) partial unmixing method. With this method, pixels with high MF scores and low MT scores were identified as candidate locations of possible unlawful buildings. A case study in the Mangshan Tombs, China demonstrated that this method had a better accuracy for identifying constructions than the commonly used fully-constrained linear unmixing model.


    加载中
    [1] Morgan J (2010) Saving our vanishing heritage: safeguarding endangered cultural heritage sites in the developing world. Palo Alto: Global Heritage Fund, 37-42.
    [2] Lasaponara R, Masini N (2006) On the potential of QuickBird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. doi: 10.1080/01431160500333983
    [3] De Laet V, Paulissen E, Waelkens M (2007) Methods for the extraction of archaeological features from very high-resolution Ikonos-2 remote sensing imagery, Hisar (southwest Turkey). J Archaeol Sci 34: 830-841. doi: 10.1016/j.jas.2006.09.013
    [4] Malinverni ES, Fangi G (2009) Comparative cluster analysis to localize emergencies in archaeology. J Cult Herit 10: e10-e19. doi: 10.1016/j.culher.2009.07.004
    [5] Weng Q (2012) Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens Environ 117: 34-49. doi: 10.1016/j.rse.2011.02.030
    [6] Epsteln J, Payne K, Kramer E (2002) Techniques for mapping suburban sprawl. Photogramm eng remote sens 63: 913-918.
    [7] Asner GP, Heidebrecht KB (2005) Desertification alters regional ecosystem–climate interactions. Glob Change Biol 11:182-194. doi: 10.1111/j.1529-8817.2003.00880.x
    [8] Garcia M, Ustin SL (2001) Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California. IEEE Trans Geosci Remote Sens 39: 1480-1490. doi: 10.1109/36.934079
    [9] Xu S, Peddle DR, Coburn CA, et al. (2008) Sensitivity of a carbon and productivity model to climatic, water, terrain, and biophysical parameters in a Rocky Mountain watershed. Can J Remote Sens 34: 245-258.
    [10] Gilabert MA, Garcıa-Haro FJ, Melia J. (2000) A mixture modeling approach to estimate vegetation parameters for heterogeneous canopies in remote sensing. Remote Sens Environ 72: 328-345. doi: 10.1016/S0034-4257(99)00109-1
    [11] Hestir Erin L, Khanna S, Andrew ME, et al. (2008) Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sens Environ 112: 4034-4047. doi: 10.1016/j.rse.2008.01.022
    [12] Röder A, Udelhoven T, Hill J, et al. (2008) Trend analysis of Landsat-TM and-ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece. Remote Sens Environ 112: 2863-2875. doi: 10.1016/j.rse.2008.01.018
    [13] Lelong CC, Pinet PC, Poilvé H (1998) Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote sens environ 66: 179-191. doi: 10.1016/S0034-4257(98)00049-2
    [14] Liu J, Miller JR, Haboudane D, et al. (2008) Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices. Can J Remote Sens 34: S124-S138. doi: 10.5589/m07-062
    [15] Lobell DB, Asner GP (2004) Cropland distributions from temporal unmixing of MODIS data. Remote Sens Environ 93: 412-422. doi: 10.1016/j.rse.2004.08.002
    [16] Pacheco A, McNairn H (2010) Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping. Remote Sens Environ 114: 2219-2228. doi: 10.1016/j.rse.2010.04.024
    [17] Eckmann TC, Still CJ, Roberts DA, et al. (2010) Variations in subpixel fire properties with season and land cover in Southern Africa. Earth Interact 14 : 1-29.
    [18] Jia GJ, Burke IC, Goetz AF, et al. (2006) Assessing spatial patterns of forest fuel using AVIRIS data. Remote Sens Environ 102: 318-327. doi: 10.1016/j.rse.2006.02.025
    [19] Katra I and Lancaster N (2008) Surface-sediment dynamics in a dust source from spaceborne multispectral thermal infrared data. Remote sens Environ 112: 3212-3221. doi: 10.1016/j.rse.2008.03.016
    [20] Goodwin N, Coops N C, Stone C (2005) Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances. Int J Appl Earth Obs Geoinform 7: 11-28. doi: 10.1016/j.jag.2004.10.003
    [21] Peddle DR, Hall FG, LeDrew EF (1999) Spectral mixture analysis and geometric-optical reflectance modeling of boreal forest biophysical structure. Remote Sens Environ 67: 288-297. doi: 10.1016/S0034-4257(98)00090-X
    [22] Soenen SA, Peddle DR, Hall RJ, et al. (2010) Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sens Environ 114: 1325-1337. doi: 10.1016/j.rse.2009.12.012
    [23] Mertes LA, Smith MO, Adams JB (1993) Estimating suspended sediment concentrations in surface waters of the Amazon River wetlands from Landsat images. Remote Sens Environ 43: 281-301. doi: 10.1016/0034-4257(93)90071-5
    [24] Svab E, Tyler AN, Preston T, et al. (2005) Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations. Int J Remote Sens 26: 919-928. doi: 10.1080/0143116042000274087
    [25] Bedini E (2009) Mapping lithology of the Sarfartoq carbonatite complex, southern West Greenland, using HyMap imaging spectrometer data. Remote Sens Environ 113: 1208-1219. doi: 10.1016/j.rse.2009.02.007
    [26] Phinn S, Stanford M, Scarth P, et al. (2002) Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. Int J Remote Sens 23: 4131-4153. doi: 10.1080/01431160110114998
    [27] Rashed T, Weeks J R, Roberts D, et al. (2003) Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogramm Eng Remote Sens 69: 1011-1020. doi: 10.14358/PERS.69.9.1011
    [28] Small C (2001) Estimation of urban vegetation abundance by spectral mixture analysis. Int J remote sens 22: 1305-1334. doi: 10.1080/01431160151144369
    [29] Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sens Environ 89: 467-483. doi: 10.1016/j.rse.2003.11.005
    [30] Wu C, Murray AT (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote sens Environ 84: 493-505. doi: 10.1016/S0034-4257(02)00136-0
    [31] Franke J, Roberts DA, Halligan K, et al. (2009) Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sens Environ 113: 1712-1723. doi: 10.1016/j.rse.2009.03.018
    [32] Powell RL, Roberts DA, Dennison PE, et al. (2007) Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sens Environ 106: 253-267. doi: 10.1016/j.rse.2006.09.005
    [33] Adams JB, Sabol DE, Kapos V, et al. (1995) Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sens Environ 52: 137-154. doi: 10.1016/0034-4257(94)00098-8
    [34] Atkinson PM, Cutler ME, LewisH (1997) Mapping sub-pixel proportional land cover with AVHRR imagery. Int J Remote Sens 18: 917-935. doi: 10.1080/014311697218836
    [35] Atkinson PM (2009) Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study. Int J Remote Sens 30: 5293-5308. doi: 10.1080/01431160903131034
    [36] Foody GM (2002) The role of soft classification techniques in the refinement of estimates of ground control point location. Photogramm Eng Remote Sens 68: 897-904.
    [37] Mertens KC, Verbeke LPC, Ducheyne EI, et al. (2003) Using genetic algorithms in sub-pixel mapping. Int J Remote Sens 24: 4241-4247. doi: 10.1080/01431160310001595073
    [38] Boardman JW, Kruse FA (2011) Analysis of Imaging Spectrometer Data Using-Dimensional Geometry and a Mixture-Tuned Matched Filtering Approach. IEEE Trans Geosci Remote Sens 49: 4138-4152 doi: 10.1109/TGRS.2011.2161585
    [39] Plaza A, Martínez P, Pérez R, et al. (2004) A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE trans geosci remote sens 42: 650-663. doi: 10.1109/TGRS.2003.820314
    [40] Goodarzi Mehr S, Ahadnejad V, Abbaspour RA, et al. (2013) Using the mixture-tuned matched filtering method for lithological mapping with Landsat TM5 images. Int J Remote Sens 34: 8803-8816. doi: 10.1080/01431161.2013.853144
    [41] Mundt JT, Streutker DR, Glenn NaF (2007) Partial unmixing of hyperspectral imagery: theory and methods. Proc Am Soc Photogramm Remote Sens.
    [42] Mitchell JJ, Glenn NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.). Int J Remote Sens 30: 6099-6119. doi: 10.1080/01431160902810620
    [43] Chen G, Zheng Z, "The Oriental Pyramid" Mangshan Tombs now total 972 mass graves. China News, 2009. Available from: http://www.chinanews.com/cul/news/2009/09-22/1879934.shtml.
    [44] Harsanyi JC, Chang CI (1994) Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans geosci remote sens 32: 779-785. doi: 10.1109/36.298007
    [45] Harris A, Bryant RG (2009) A multi-scale remote sensing approach for monitoring northern peatland hydrology: Present possibilities and future challenges. J environ manag 90: 2178-2188. doi: 10.1016/j.jenvman.2007.06.025
    [46] Brelsford C, Shepherd D (2014) Using mixture-tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada. J Appl Remote Sens 8: 083660-083660. doi: 10.1117/1.JRS.8.083660
    [47] Boardman JW, Kruse F A, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data.
    [48] Harris, Mixture Tuned Matched Filtering. Harris Geospatial Solutions,2016. Available from: https://www.harrisgeospatial.com/docs/mtmf.html.
    [49] Lu D, Weng Q (2004) Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm Eng Remote Sens 70: 1053-1062. doi: 10.14358/PERS.70.9.1053
    [50] Parker AE, Hunt ER (2004) Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J Range Manag 57: 106-112. doi: 10.2307/4003961
    [51] Mundt JT, Glenn NF, Weber K T, et al. (2005) Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques. Remote Sens Environ 96: 509-517. doi: 10.1016/j.rse.2005.04.004
    [52] Snyder RA, Boss CL (2002) Recovery and stability in barrier island plant communities. J Coast Res 18: 530-536.
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3740) PDF downloads(823) Cited by(1)

Article outline

Figures and Tables

Figures(11)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog