面向机载高光谱数据的3D-CNN亚热带森林树种分类

doi:10.11707/j.1001-7488.20201110

摘要/Abstract

摘要：

目的: 探讨深度卷积神经网络在机载高光谱数据分类中的应用，以提高亚热带地区森林树种分类精度。方法: 以广西南宁高峰林场为试验区，基于中国林业科学研究院LiCHy系统获取的机载高光谱数据，以三维卷积层为基础，提出一种高效的卷积神经网络(CNN)结构。CNN模型以端到端方式处理高光谱影像分析问题，将原始数据作为输入，不需要降维或特征筛选，可省去传统分类方法在不同程度上人工筛选特征的工作；网络中3D卷积层可同时提取光谱特征和空间特征，学习特征立方体空间和光谱维度的局部信号变化，利用重要的识别特征进行分类，以提高对高光谱影像的判别能力。针对机载高光谱数据维度高、训练样本相对较少的问题，对模型进行优化，以避免过拟合。结果: 相较传统的特征筛选与面向对象分割结合的方法，本研究提出的3D-CNN结构森林树种总体分类精度达98.38%，Kappa系数为0.98，与随机森林特征选择结合支持向量机分类相比，总体精度提高8.82%，Kappa系数提高0.11；小样本训练情况下(减少75%训练样本)，总体精度仍可达95.89%，Kappa系数为0.94。结论: 三维卷积神经网络在处理机载高光谱影像特征提取和分类问题时能够充分利用影像中的丰富信息，实现高精度区分亚热带森林树种；合理的网络结构以及训练策略(加入Dropout)能够极大提高网络训练速度，并在小样本训练时仍能得到很好的结果，可实现高效、准确的森林树种分类。

关键词: 高光谱遥感, 卷积神经网络, 树种分类, 小样本

Abstract:

Objective: This study was implemented to explore the potential of deep convolutional neural networks in airborne hyperspectral data classification,so as to improve the classification accuracy of forest tree species in subtropical regions. Method: The aeronautical hyperspectral data of Nanning Gaofeng forest farm in Guangxi Zhuang Autonomous Region obtained by the LiCHy system of Chinese Academy of Forestry was used. The CNN model used in this paper aimed to deal with hyperspectral image analysis problems in an end-to-end manner. It could take raw data as input,without dimension reduction or feature screening,eliminating the need for traditional classification methods to manually feature selection in different degrees. The 3D convolutional layers in the network could extract spectral features and spatial features simultaneously,learn the local signal changes in the spatial and spectral dimensions of the feature cube,and classify them with important recognition features to improve the discriminating ability of hyperspectral images. For the problem of high dimensionality of airborne hyperspectral data and relatively few training samples,the CNN model was optimized to avoid over-fitting. Result: Compared with the traditional feature selection and object-oriented segmentation method,CNN could obtain a higher classification accuracy,the overall accuracy reached 98.38%,Kappa coefficient was 0.98. Compared with support vector machine combined with random forest (RF) feature selection classification,the overall accuracy was improved by 8.82%,and the Kappa coefficient was increased by 0.11. In the case of small sample training (75% reduction in training samples size),the overall accuracy still reached 95.89%,and the Kappa coefficient was 0.94. Conclusion: The three-dimensional convolutional neural network could fully utilize the rich information in the image processing of the feature extraction and classification of airborne hyperspectral imagery,which could achieve high-precision discrimination of subtropical forest tree species; in addition,reasonable network structure and training strategy (adding the Dropout layer) could greatly improve the network training speed and still get good results in small sample training,and could achieve efficient and accurate classification of forest species.

Key words: hyperspectral remote sensing, convolutional neural network, tree species classification, small sample

中图分类号:

S757

赵霖,张晓丽,吴艳双,张斌. 面向机载高光谱数据的3D-CNN亚热带森林树种分类[J]. 林业科学, 2020, 56(11): 97-107.

Lin Zhao,Xiaoli Zhang,Yanshuang Wu,Bin Zhang. Subtropical Forest Tree Species Classification Based on 3D-CNN for Airborne Hyperspectral Data[J]. Scientia Silvae Sinicae, 2020, 56(11): 97-107.

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参数Parameter	数值Value
光谱范围Spectral range	400~1 000 nm
光谱分辨率Spectral resolution	3.3 nm
视场角Field of view	37.7°
瞬间视场角Instantaneous field of view	0.646 m·rad
焦距Focal length	18.1 mm
波段数Number of band	125
空间像元数Number of spatial pixel	1 024
光谱采样间隔Sampling interval in spectral	4.6 nm
量化值Bit depth	12 bits

网络层 Layer	卷积核大小 Filter size	卷积核数量 Number of filter	步长 Stride	输出大小 Output size	特征立方体数量 Feature volumes	参数数量 Number of parameters
输入层Input	—	—		11×11×125	1	0
3D卷积层1 3D_CONV Layer1	3×3×7	1×N	1, 1, 1	9×9×119	4	256
3D卷积层2 3D_CONV Layer2	3×3×7	N×2N	1, 1, 1	7×7×113	8	2 024
3D卷积层3 3D_CONV Layer3	3×3×7	2N×N×4N	1, 1, 1	5×5×117	16	8 080
3D卷积层4 3D_CONV Layer4	3×3×7	8N×N	1, 1, 1	3×3×111	32	32 288
全连接层 Fully_Connect_Layer	—	—	—	1×1×1	128	3 723 392
Softmax层 Softmax	—	—	—	1×1×1	12	1 548

特征Feature	数量Number	方法Method
光谱变换 Spectral transform	5	ICA(独立成分分析)变换前5个主成分ICA (independent component analysis)top 5 principal components
光谱指数 Spectral indices	9	NDVI(Bannari et al., 1995)、PSRI(Merzlyak et al., 1999)、MRESRI(Sims et al., 2002)、 MRENDVI(Sims et al., 2002)、GNDVI(Lichtenthaler et al., 1996)、 PRI(Hernández-Clemente et al., 2011)、SIPI(Haboudane et al., 2002)、 ARI1(Gitelson et al., 2001)、VOG1(Xue et al., 2017)
纹理特征 Texture feature	24	高光谱图像的第482、550和650波段通过GLCM(灰度共生矩阵)获取纹理特征, 纹理窗口大小为17×17，得到24个纹理特征变量The 482^th, 550^th and 650^th bands of hyperspectral images obtain texture features by GLCM(gray level co-occurrence matrix). The texture window size is 17×17, and 24 texture feature variables are obtained
高度特征Height feature	1	利用LiCHy系统同步获取的LiDAR数据得到数字冠层高度模型作为高度特征变量，以确定各树种的高度信息Using the LiDAR data obtained synchronously by LiCHy system, the digital canopy height model is obtained as the height characteristic variable to determine the height information of each tree species

序号 No.	地物类别Categories	样本Samples
序号 No.	地物类别Categories	训练样本Trian	验证样本Validation	测试样本Test
1	杉木Cunninghamia lanceolata	8 074	8 074	64 594
2	马尾松Pinus massoniana	534	534	4 272
3	湿地松Pinus elliottii	417	417	3 340
4	巨尾桉Eucalyptus grandis × E.urophylla	1 493	1 493	11 945
5	尾叶桉Eucalyptus urophylla	4 027	4 027	32 220
6	红椎Castanopsis hystrix	2 546	2 546	20 369
7	米老排Mytilaria laosensis	300	300	2 400
8	油茶Camellia oleifera	832	832	6 659
9	其他阔叶树Other broadleaf forest	1 074	1 074	8 593
10	道路Road	1 047	1 047	8 378
11	采伐迹地Cutting site	1 017	1 017	8 136
12	建筑用地Building land	7	7	54
	总计Total	21 369	21 369	170 960

项目Item	ICA+光谱指数+纹理特征 ICA+ spectral indices+ texture feature	随机森林特征筛选 Random forest feature screening	2D-CNN (W=9)	2D-CNN (W=27)	3D-CNN (W=5, N=4)	3D-CNN (W=9, N=4)	3D-CNN (W=11, N=4)	3D-CNN (W=11, N=8)	3D-CNN (W=13, N=8)
总体精度Overall accuracy(%)	86.66	89.56	75.16	91.62	93.90	96.82	98.38	98.44	98.37
平均精度Average accuracy(%)	74.73	80.03	59.96	81.53	93.64	97.39	98.80	98.74	98.92
Kappa系数(K×100)	83.03	86.87	67.49	89.29	92.27	95.99	97.96	98.03	97.94
杉木 Cunninghamia lanceolata	90.63	93.55	83.41	90.66	93.93	96.93	98.37	98.52	97.88
马尾松 Pinus massoniana	34.76	32.38	0	62.37	93.32	98.40	99.19	99.24	99.05
湿地松 Pinus elliottii	75.21	78.37	36.16	77.69	89.16	97.25	99.06	98.74	99.79
巨尾桉 Eucalyptus grandis × E.urophylla	97.83	94.02	68.12	96.43	97.10	98.35	99.28	99.61	99.44
尾叶桉 Eucalyptus urophylla	98.16	91.47	61.82	93.05	95.37	98.83	99.67	99.69	99.78
红椎 Castanopsis hystrix	85.59	70.71	57.23	86.86	85.25	89.61	94.32	94.88	95.36
米老排 Mytilaria laosensis	47.38	10.95	69.20	88.29	89.81	97.75	98.95	99.34	99.00
油茶 Camellia oleifera	98.72	98.60	79.14	91.49	97.62	99.44	99.72	99.91	99.75
其他阔叶树 Other broadleaf forest	75.27	75.20	79.52	95.35	94.03	95.99	97.46	95.43	97.44
道路Road	90.99	88.05	98.53	98.02	99.65	99.29	99.65	99.61	99.65
采伐迹地Cutting site	99.87	99.88	86.37	98.16	98.76	99.10	99.96	99.94	99.94
建筑用地Buliding land	65.91	63.64	0	0	89.66	97.78	100.00	100.00	100.00
需要训练参数的数量 Number of training parameters	—	—	251 244	202 092	507 172	458 020	3 767 588	7 618 172	10 386 724
预测需要的时间 Time required for predicting	—	—	35	39	29	44	62	87	92
训练所需要的时间 Time required for training	—	—	14.5	13.0	N/A	35.5	61.5	90.0	101.5