Scientia Silvae Sinicae ›› 2020, Vol. 56 ›› Issue (11): 97-107.doi: 10.11707/j.1001-7488.20201110
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Lin Zhao,Xiaoli Zhang*,Yanshuang Wu,Bin Zhang
Received:
2019-02-11
Online:
2020-11-25
Published:
2020-12-30
Contact:
Xiaoli Zhang
CLC Number:
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.
Table 1
The main parameters of AISA EagleⅡ hyperspectral system of LiCHy"
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 |
Table 2
Optimal network structure parameters (input W=11, N=4)"
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 |
Table 3
Image object metrics used in tree species classification"
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( MRENDVI( PRI( ARI1( |
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 482th, 550th and 650th 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 |
Table 4
Summary of samples for training, validation and testing for each land cover class"
序号 No. | 地物类别Categories | 样本Samples | ||
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 |
Table 5
Comparisons of accuracy assessment results among different methods"
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 |
李竺强, 朱瑞飞, 高放, 等. 三维卷积神经网络模型联合条件随机场优化的高光谱遥感影像分类. 光学学报, 2018, 38 (8): 404- 413. | |
Li Z Q , Zhu R F , Gao F , et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization. Acta Optica Sinica, 2018, 38 (8): 404- 413. | |
荚文, 庞勇, 岳彩荣, 等. 机载AISA EagleⅡ高光谱数据处理——以额济纳旗试验区为例. 遥感技术与应用, 2016, 31 (3): 504- 510. | |
Jia W , Pang Y , Yue C R , et al. The procesing of airborne AISA EagleⅡ data in Ejina Banner study area. Remote Sensing Technology and Application, 2016, 31 (3): 504- 510. | |
毛学刚, 陈文曲, 魏晶昱, 等. 分割尺度对面向对象树种分类的影响及评价. 林业科学, 2017, 53 (12): 73- 83.
doi: 10.11707/j.1001-7488.20171208 |
|
Mao X G , Chen W Q , Wei J Y , et al. Effect and evaluation of segmentation scale on object-based forest species classification. Scientia Silvae Sinicae, 2017, 53 (12): 73- 83.
doi: 10.11707/j.1001-7488.20171208 |
|
张莹, 张晓丽, 李宏志, 等. 基于改进转换分离度特征选择规则的土地覆盖分类比较. 林业科学, 2018, 54 (8): 88- 98. | |
Zhang Y , Zhang X L , Li H Z , et al. A comparison of landcover classification based on the improved transformed divergence analysis. Scientia Silvae Sinicae, 2018, 54 (8): 88- 98. | |
Bannari A , Morin D , Bonn F , et al. A review of vegetation indices. Remote Sensing Reviews, 1995, 13 (1/2): 95- 120. | |
Chen Y , Jiang H , Li C , et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (10): 6232- 6251.
doi: 10.1109/TGRS.2016.2584107 |
|
Girshick R , Donahue J , Darrell T , et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, 580- 587. | |
Gitelson A A , Merzlyak M N . Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol, 2001, 74 (1): 38- 45. | |
Haboudane D , Miller J R , Tremblay N , et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81 (2/3): 416- 426. | |
Hernández-Clemente R , Navarro-Cerrillo R M , Suárez L , et al. Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 2011, 115 (9): 2360- 2375.
doi: 10.1016/j.rse.2011.04.036 |
|
Hinton G E , Osindero S , Teh Y W . A fast learning algorithm for deep belief nets. Neural Computation, 2006a, 18 (7): 1527- 1554.
doi: 10.1162/neco.2006.18.7.1527 |
|
Hinton G E , Salakhutdinov R R . Reducing the dimensionality of data with neural networks. Science, 2006b, 313 (5786): 504- 507.
doi: 10.1126/science.1127647 |
|
Hinton G E, Srivastava N, Krizhevsky A, et al. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv: 1207.0580. | |
Ji S , Xu W , Yang M , et al. 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (1): 221- 231.
doi: 10.1109/TPAMI.2012.59 |
|
Kobayashi S , Sanga-Ngoie K . A comparative study of radiometric correction methods for optical remote sensing imagery: the IRC vs. other image-based C-correction methods. International Journal of Remote Sensing, 2009, 30 (2): 285- 314. | |
Krizhevsky A , Sutskever I , Hinton G E . Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 25 (2): 1097- 1105. | |
Le Roux N , Bengio Y . Deep belief networks are compact universal approximators. Neural Computation, 2010, 22 (8): 2192- 2207.
doi: 10.1162/neco.2010.08-09-1081 |
|
Li Y , Zhang H , Shen Q . Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 2017, 9 (1): 67. | |
Lichtenthaler H K , Lang M , Sowinska M , et al. Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 1996, 148 (5): 599- 612. | |
Makantasis K , Karantzalos K , Doulamis A , et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks. Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, 2015, 4959- 4962. | |
Merzlyak M N , Gitelson A A , Chivkunova O B , et al. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 1999, 106 (1): 135- 141. | |
Simonyan K, SZisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint: 1409.1556. | |
Sims D A , Gamon J A . Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81 (2/3): 337- 354. | |
Szegedy C , Liu W , Jia Y , et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1- 9. | |
Tran D , Bourdev L , Fergus R T , et al. Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, 2015, 4489- 4497. | |
Vincent P , Larochelle H , Lajoie I , et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11 (12): 3371- 3408. | |
Xue J , Su B . Significant remote sensing vegetation indices: a review of developments and applications. Journal of Sensors, 2017, 2017, 1- 17. | |
Yue J , Zhao W , Mao S , et al. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters, 2015, 6 (6): 468- 477. |
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