Extraction of metamorphic minerals by multiscale segmentation combined with random forest

TANG Shu-lan; MENG Yong; WANG Guo-qiang; BU Tao

doi:10.13374/j.issn2095-9389.2020.09.08.004

Volume 44 Issue 2

Feb. 2022

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TANG Shu-lan, MENG Yong, WANG Guo-qiang, BU Tao. Extraction of metamorphic minerals by multiscale segmentation combined with random forest[J]. Chinese Journal of Engineering, 2022, 44(2): 170-179. doi: 10.13374/j.issn2095-9389.2020.09.08.004

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TANG Shu-lan, MENG Yong, WANG Guo-qiang, BU Tao. Extraction of metamorphic minerals by multiscale segmentation combined with random forest[J]. Chinese Journal of Engineering, 2022, 44(2): 170-179. doi: 10.13374/j.issn2095-9389.2020.09.08.004

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Extraction of metamorphic minerals by multiscale segmentation combined with random forest

doi: 10.13374/j.issn2095-9389.2020.09.08.004

1.
School of Management, Xi’an University of Finance and Economics, Xi’an 710100, China
2.
Xi’an Center of Geological Survey, China Geological Survey, Xi’an 710054, China

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Corresponding author: E-mail: 16392800@qq.com
Received Date: 2020-09-08
Available Online: 2021-01-08
Publish Date: 2022-02-15

Abstract

Abstract

The identification of metamorphic minerals is the basis of metamorphic rock research. Extraction of mineral information by remote sensing technology has been widely used. Digital image processing technology is also effectively applied to remote sensing image processing. Results show that the band ratio of remote sensing images can enhance mineral information, while the variogram function can describe the spatial correlation and variability of image pixels and extract more detailed texture information. The metamorphic minerals are found to present a block or strip distribution. The object-oriented remote sensing image information extraction method can avoid the “salt and pepper phenomenon” based on pixel extraction. Meanwhile, the random forest classification method has a fast calculation speed and high parameter accuracy. It is not sensitive to the noise caused by more lithologic components and its classification effect is found to be stable. To improve the extraction accuracy of metamorphic minerals from remote sensing images and further improve the recognition effect of metamorphic zones, this paper combined the ratio operation, multiscale segmentation, and random forest classification to extract metamorphic mineral information from ASTER images in Beishan area in Gansu Province. Initially, the image was enhanced by the ratio formula of the characteristic spectral structure of the target mineral. Multiscale image segmentation was then performed based on the spectrum and variogram. Finally, the accuracy was evaluated by the thin film identification results of the field exploration samples after the extraction of the target mineral by random forest. Results show that biotite, muscovite, and amphibole have identification characteristics on the ASTER image with an extraction accuracy of 85.4088%, 84.7640%, and 85.7308%, respectively. The extraction accuracy of other metamorphic minerals with less content are found to reach more than 60%. Multiscale segmentation can make full use of the clustering features of minerals and the variogram texture can enhance the ability of morphological features to distinguish the minerals. Random forest is not sensitive to noise and the extraction results are observed to be stable.
- variogram,
- multiscale segmentation,
- ASTER,
- mineral extraction,
- random forest

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References(22)

References

[1]	Diener J F A, White R W, Link K, et al. Clockwise, low-P metamorphism of the Aus qranulite terrain, southern Namibia, during the Mesoproterozoic Namaqua Oroqeny. Precambrian Res, 2013, 224: 629 doi: 10.1016/j.precamres.2012.11.009
[2]	謝明輝, 張奇, 陳圣波, 等. 基于特征導向主成分分析遙感蝕變異常提取方法. 地球科學—中國地質大學學報, 2015, 40(8):1381 Xie M H, Zhang Q, Chen S B, et al. Extraction of alteration anomaly information by feature-based principal component analysis from ASTER data. Editorial Committe Earth Sci J China Univ Geosci, 2015, 40(8): 1381
[3]	Zadeh M H, Tangestani M H, Roldan F V, et al. Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data. Adv Space Res, 2014, 53(3): 440 doi: 10.1016/j.asr.2013.11.029
[4]	吳志春, 葉發旺, 郭福生, 等. 主成分分析技術在遙感蝕變信息提取中的應用研究綜述. 地球信息科學學報, 2018, 20(11):1644 doi: 10.12082/dqxxkx.2018.180195 Wu Z C, Ye F W, Guo F S, et al. A review on application of techniques of principle component analysis on extracting alteration information of remote sensing. J Geo-Inf Sci, 2018, 20(11): 1644 doi: 10.12082/dqxxkx.2018.180195
[5]	劉義志, 賴華榮, 張丁旺, 等. 多特征混合核 SVM 模型的遙感影像變化檢測. 國土資源遙感, 2019, 31(1):16 Liu Y Z, Lai H R, Zhang D W, et al. Change detection of high resolution remote sensing image alteration based on multi-feature mixed kernel SVM model. Remote Sens Land Resour, 2019, 31(1): 16
[6]	何中海, 何彬彬. 基于權重光譜角制圖的高光譜礦物填圖方法. 光譜學與光譜分析, 2011, 31(8):2200 doi: 10.3964/j.issn.1000-0593(2011)08-2200-05 He Z H, He B B. Weight spectral angle mapper (WSAM) method for hyperspectral mineral mapping. Spectrosc Spectr Anal, 2011, 31(8): 2200 doi: 10.3964/j.issn.1000-0593(2011)08-2200-05
[7]	Kaur S, Bansal R K, Mittal M, et al. Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. J Indian Soc Remote Sens, 2019, 47(3): 427 doi: 10.1007/s12524-019-00946-2
[8]	馮文卿, 眭海剛, 涂繼輝, 等. 高分辨率遙感影像的隨機森林變化檢測方法. 測繪學報, 2017, 46(11):1880 doi: 10.11947/j.AGCS.2017.20170074 Feng W Q, Sui H G, Tu J H, et al. Change detection method for high resolution remote sensing images using random forest. Acta Geodaet Cartograph Sin, 2017, 46(11): 1880 doi: 10.11947/j.AGCS.2017.20170074
[9]	Booysen R, Zimmermann R, Lorenz S, et al. Towards multiscale and multisource remote sensing mineral exploration using RPAS: a case study in the Lofdal carbonatite-hosted REE deposit, Namibia. Remote Sens, 2019, 11(21): 2500 doi: 10.3390/rs11212500
[10]	Cid Y D, Muller H, Platon A, et al. 3D solid texture classification using locally-oriented wavelet transforms. IEEE Trans Image Process, 2017, 26(4): 1899 doi: 10.1109/TIP.2017.2665041
[11]	王猛, 張新長, 王家耀, 等. 結合隨機森林面向對象的森林資源分類. 測繪學報, 2020, 49(2):235 doi: 10.11947/j.AGCS.2020.20190272 Wang M, Zhang X C, Wang J Y, et al. Forest resource classification based on random forest and object oriented method. Acta Geodaet Cartograph Sin, 2020, 49(2): 235 doi: 10.11947/j.AGCS.2020.20190272
[12]	游永發, 王思遠, 王斌, 等. 高分辨率遙感影像建筑物分級提取. 遙感學報, 2019, 23(1):125 You Y F, Wang S Y, Wang B, et al. Study on hierarchical building extraction from high resolution remote sensing imagery. J Remote Sens, 2019, 23(1): 125
[13]	李江昀, 趙義凱, 薛卓爾, 等. 深度神經網絡模型壓縮綜述. 工程科學學報, 2019, 41(10):1229 Li J Y, Zhao Y K, Xue Z E, et al. A survey of model compression for deep neural networks. Chin J Eng, 2019, 41(10): 1229
[14]	Cracknell M J, Reading A M. Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput Geosci, 2014, 63: 22 doi: 10.1016/j.cageo.2013.10.008
[15]	Harris J. R, He J X, Rainbird R, et al. A comparison of different remotely sensed data for classifying bedrock types in Canada’s arctic: application of the robust classification method and random forests. Geosci Can, 2014, 41(4): 557
[16]	Hossain M D, Chen D M. Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS J Photogramm Remote Sens, 2019, 150: 115 doi: 10.1016/j.isprsjprs.2019.02.009
[17]	Diaz G F, Ortiz J M, Silva J F, et al. Variogram-based descriptors for comparison and classification of rock texture images. Math Geosci, 2020, 52(4): 451 doi: 10.1007/s11004-019-09833-5
[18]	Zhang L, Liu Z, Ren T W, et al. Identification of seed maize fields with high spatial resolution and multiple spectral remote sensing using random forest classifier. Remote Sens, 2020, 12(3): 362 doi: 10.3390/rs12030362
[19]	朱俊杰, 范湘濤, 杜小平. 幾何特征表達及基于幾何特征的建筑物提取. 應用科學學報, 2015, 33(1):9 doi: 10.3969/j.issn.0255-8297.2015.01.002 Zhu J J, Fan X T, Du X P. Geometric feature representation and building extraction based on geometric features. J Appl Sci, 2015, 33(1): 9 doi: 10.3969/j.issn.0255-8297.2015.01.002
[20]	Masoumi F, Eslamkish T, Abkar A A, et al. Integration of spectral, thermal, and textural features of ASTER data using random forests classification for lithological mapping. J Afric Earth Sci, 2017, 129: 445 doi: 10.1016/j.jafrearsci.2017.01.028
[21]	Pournamdari M, Hashim M, Pour A B. Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, South Iran. Adv Space Res, 2014, 54(4): 694 doi: 10.1016/j.asr.2014.04.022
[22]	張博, 何彬彬. 改進的分水嶺變換算法在高分辨率遙感影像多尺度分割中的應用. 地球信息科學學報, 2014, 16(1):142 Zhang B, He B B. Multi-scale segmentation of high-resolution remote sensing image based on improved watershed transformation. J Geo-Inf Sci, 2014, 16(1): 142