结合多尺度分割和随机森林的变质矿物提取

唐淑兰; 孟勇; 王国强; 卜涛

doi:10.13374/j.issn2095-9389.2020.09.08.004

结合多尺度分割和随机森林的变质矿物提取

doi: 10.13374/j.issn2095-9389.2020.09.08.004

唐淑兰¹,
孟勇^2, ,,
王国强²,
卜涛²

1.
西安财经大学管理学院，西安 710100
2.
中国地质调查局西安地质调查中心，西安 710054

基金项目: 中国地质调查局资助项目（DD20179403；DD20190364；DD20190812）；陕西省教育厅资助项目（18JK0317）；陕西省自然科学基础研究计划资助项目（2020JM-585）

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E-mail：16392800@qq.com

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出版历程
- 收稿日期: 2020-09-08
- 网络出版日期: 2021-01-08

Extraction of metamorphic minerals by multiscale segmentation combined with random forest

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

More Information

Corresponding author: E-mail: 16392800@qq.com

摘要

摘要: 为提高遥感影像变质矿物提取精度，提升变质带的识别效果，以甘肃北山ASTER影像为研究区，结合了比值运算、多尺度分割、随机森林分类法进行变质矿物提取。首先，通过矿物特征性光谱特征构造比值运算公式、进行影像增强；然后，对增强影像进行基于光谱及变差函数的多尺度分割；接着，采用随机森林法提取目标矿物；最后，通过野外勘查、采样、薄片鉴定进行精度评价。结果表明，黑云母、白云母、角闪石在ASTER影像上具有鉴定性特征，提取精度分别为85.4088%、84.7640%和85.7308%；其他含量较少的变质矿物提取精度可达到60%以上。多尺度分割能充分利用矿物的丛集特征；变差函数纹理能增强形态特征对矿物的区分能力；随机森林分类法对矿物混合引起的噪声不敏感、提取结果稳定。
- 变差函数 /
- 多尺度分割 /
- ASTER /
- 矿物提取 /
- 随机森林
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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图 1 研究区地质简图

Figure 1. Geological sketch of the study area

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图 2 标志性矿物反射率曲线

Figure 2. Reflectance curve of marker minerals

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图 3 技术流程

Figure 3. Technical process

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图 4 比值增强结果。（a）Bi；（b）Mus；（c）Am；（d）Chl；（e）Gt；（f）Act

Figure 4. Ratio enhancement results: (a) Bi; (b) Mus; (c) Am; (d) Chl; (e) Gt; (f) Act

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图 5 多尺度分割流程

Figure 5. Multiscale segmentation process

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图 6 GS值变化趋势

Figure 6. Change trend of GS values

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图 7 多尺度分割结果。（a）Bi；（b）Mus；（c）Am；（d）Chl；（e）Gt；（f）Act

Figure 7. Multiscale segmentation results: (a) Bi; (b) Mus; (c) Am; (d) Chl; (e) Gt; (f) Act

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图 8 特征重要程度排名

Figure 8. Ranking of feature importance

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图 9 决策树个数选择

Figure 9. Number selection of decision trees

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图 10 矿物提取结果。（a）Bi；（b）Mus；（c）Am；（d）Chl；（e）Gt；（f）Act

Figure 10. Mineral extraction results: (a) Bi; (b) Mus; (c) Am; (d) Chl; (e) Gt; and (f) Act

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图 11 部分样品显微照片。（a）云母石英片岩；（b）石榴石白云母斜长片麻岩；（c）蛇纹石透辉石白云质大理岩；（d）钠长石绿泥石石英千枚岩

Figure 11. Micrograph of some samples: (a) mica quartz schist; (b) garnet muscovite plagioclase gneiss; (c) serpentine diopside dolomitic marble; (d) albite chlorite quartz phyllite

Q—Quartz；Pl—Plagioclase；Kf—K-feldspar；Ser—Sericite；Am—Amphibole；Bi—Biotite；Di—Diopside；Act—Actinolite；Serp—Serpentine，Cc—Carbonate minerals；Mus—Muscovite；Gt—Garnet；Chl—Chlorite；Ep—Epidote

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表 1 矿物的吸收谱带与ASTER波段的对应关系

Table 1. Correspondence relation between the absorption bands of the minerals and ASTER bands

Mineral	ASTER band
Mineral	1	5	6	7	8	9	10	11	12	13
Bi							Reflex	Absorption	Reflex
Mus	Absorption	Reflex	Absorption	Reflex
Am				Reflex	Absorption	Reflex
Chl	Absorption	Reflex			Absorption	Reflex
Gt									Absorption	Reflex
Act			Reflex		Absorption	Reflex

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表 2 各矿物比值公式

Table 2. Ratio formula of minerals

Bi	Mus	Am	Chl	Gt	Act
(b₁₂+b₁₀)/b₁₁	(b₅+b₇)/b₆	(b₆+b₉)/(b₈+b₇)	(b₁+b₉)/b₈	b₁₃/b₁₂	(b₆+ b₉)/b₈

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表 3 矿物样本数

Table 3. Mineral samples

Bi	Mus	Am	Chl	Gt	Act
1577	1348	1945	1403	912	832

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表 4 不同特征数量矿物提取精度

Table 4. Extraction precision of minerals with different characteristic numbers

Features	Extraction precision/%
Features	Bi	Mus	Am	Chl	Gt	Act
1	80.0041	75.1212	72.3755	66.0052	64.6564	60.0027
2	82.3421	76.2342	75.1137	67.2311	65.0033	60.0456
3	83.0302	76.7802	76.0232	67.6121	67.9111	60.0101
4	83.0012	76.8767	75.3545	67.5689	65.5889	60.01
5	82.4632	76.2069	75.1182	66.7865	65.3421	60.0043
6	81.8795	76.1951	74.1147	66.0011	65.001	59.9876
7	80.789	76.0022	73.2433	65.5045	64.7768	59.3425
8	79.6574	75.5467	72.1389	64.8675	64.3345	59.0001
9	78.6759	74.3452	71.3511	63.7865	62.9976	58.3456

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表 5 部分样品薄片鉴定结果

Table 5. Identification results of some sample slices

Sample number	Sampling location		Lithology	Mineral composition
Sample number	Longitude	Latitude	Lithology	Mineral composition
D0158	96°32′13.1″	41°59′41.4″	Albite epidote chlorite schist	Quartz 48%，Feldspar 25%，Chlorite 12%，Epidote 8%， Biotite 3%，Sphene 3%
D0126	96°32′53.1″	41°59′20.4″	Plagioclase amphibolite	Amphibole 75%，Plagioclase 15%，Quartz 5%，Carbonate minerals 4%，Opaque minerals 1%
D0123	96°32′56.6″	41°59′19.5″	Garnet muscovite plagioclase gneiss	Muscovite 30%，Feldspar 62%，Quartz 5%，Garnet 3%
D0121	96°32′57.6″	41°59′19.3″	Biotite plagioclase gneiss	Quartz 45%，Plagioclase 33%，Biotite 20%，Amphibole 2%
D0117	96°33′8.2″	41°59′17.8″	Biotite plagioclase gneiss	Quartz 28%，Plagioclase 25%，Biotite 25%，Chlorite 15%， K-feldspar 5%，Opaque minerals 2%
D0107	96°33′16.8″	41°59′7.5″	Tonalite	Quartz 42%，Feldspar 34%，Biotite 12%，Amphibole 8%，Carbonate minerals 2%，Sericite 2%
D0105	96°33′16.6″	41°59′4″	Garnet biotite plagioclase gneiss	Quartz 50%，Feldspar 31%，Biotite 10%，Garnet 5%， Opaque minerals 3%，Apatite 1%
D0103	96°33′16.3″	41°59′3″	Garnet biotite plagioclase gneiss	Quartz 40%，Plagioclase 30%，K-feldspar 7%，Biotite 12%，Garnet 3%
D1125	96°31′36.8″	41°57′24.3″	Serpentine diopside dolomitic marble	Calcite 49%，Diopside 32%，Serpentine 14%，Actinolite 5%
D0901	96°40′47.7″	41°55′39.2″	Albite chlorite quartz phyllite	Quartz 32%，Plagioclase 16%，Chlorite 48%，Epidote 4%，Sphene
D0420	96°34′35.6″	41°52′36.5″	Plagioclase amphibolite	Plagioclase 55%，Amphibole 30%，Quartz 10%，Biotite 3%，Epidote 2%
D0426	96°34′34.5″	41°52′29″	Mica quartz schist	Quartz 50%，Muscovite 30%，Biotite 20%
D0450	96°34′14.9″	41°51′38.6″	Plagioclase hornblende gneiss	Plagioclase 35%，Amphibole 55%，Epidote 5%，Quartz 5%
D1007	96°30′44.9″	41°50′45.1″	Mica monzonite gneiss	K-feldspar 30%，Plagioclase 25%，Quartz 27%， Biotite 14%，Muscovite 4%
D0055	96°33′49″	41°50′6.3″	Biotite plagioclase gneiss	Plagioclase 38%，Quartz 35%，Biotite 20%，Muscovite 3%，Chlorite 4%
D0113	96°34′19.3″	41°51′46″	Plagioclase hornblende gneiss	Amphibole 45%，Albite 37%，Microcline 10%，Quartz 5%，Pyroxene 2%，Opaque minerals 1%

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表 6 精度评价

Table 6. Accuracy evaluation

Mineral	PA/%	UA/%	OA/%	Kappa
Bi	80.95	78.64	85.4088	0.7779
Mus	83.35	75.60	84.7640	0.7833
Am	79.74	77.55	85.7308	0.7748
Chl	68.74	63.76	70.6933	0.5938
Gt	58.42	58.43	65.5992	0.5462
Act	59.61	64.41	66.7509	0.5560

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表 7 本文方法与其他方法提取精度对比

Table 7. Comparison of extraction accuracy between the present method and other methods

Mineral	Extraction accuracy/%
Mineral	Ratio + threshold segmentation	Ratio +SVM	Method of this paper
Bi	77.3546	81.1341	85.4088
Mus	81.2830	82.9870	84.7640
Am	76.9684	81.1002	85.7308
Chl	64.6594	66.4532	70.6933
Gt	61.8422	63.1886	65.5992
Act	62.7847	64.2351	66.7509

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