电子学报 ›› 2022, Vol. 50 ›› Issue (4): 932-942.DOI: 10.12263/DZXB.20210636
所属专题: 机器学习交叉融合创新
邓梦娇1, 徐新1,2, 马盈盈3, 龚威3, 金适宽3, 胡瑞敏4
收稿日期:
2021-07-17
修回日期:
2022-03-07
出版日期:
2022-04-25
作者简介:
基金资助:
DENG Meng-jiao1, XU Xin1,2, MA Ying-ying3, GONG Wei3, JIN Shi-kuan3, HU Rui-min4
Received:
2021-07-17
Revised:
2022-03-07
Online:
2022-04-25
Published:
2022-04-25
Supported by:
摘要:
云检测是卫星遥感数据预处理中至关重要的工作.本文将多层感知机和辐射传输模型相结合,利用可见光和近红外波段反射率信息从卫星影像中识别出云像元.该方法利用SBDART辐射传输模型,模拟获得了各种复杂陆地地表的反射率值数据集,为多层感知机提供训练样本.随后,用训练好的多层感知机模型区分FY-3D卫星MERSI II影像中的云像元和非云像元,利用CALIPSO垂直特性掩膜产品(Vertical Feature Mask,VFM)逐像元进行验证,并与MODIS云掩膜产品(MYD35)进行横向对比.结果表明,以VFM数据集为标准的情况下,多层感知机识别云的总正确率为76.25%,其中在夏季和低纬度地区效果最好,如赤道附近地表识别的准确率可达到91.74%,而在城市、农田和裸地等复杂地表类型条件下的云检测识别正确率分别为83.37%、84.52%和73.11%,分别高于MYD35产品的83.25%、83.31%和72.66%.为了进一步验证多层感知机结合辐射传输模型云检测方法的有效性,将辐射传输模型模拟得到的训练样本分别用于k-最近邻、朴素贝叶斯以及随机森林算法,并与本文多层感知机算法进行对比.结果表明,将多层感知机和辐射传输模型相结合具有更高的正确率.
中图分类号:
邓梦娇, 徐新, 马盈盈, 等. 多层感知机结合辐射传输模型的复杂陆地表面云检测[J]. 电子学报, 2022, 50(4): 932-942.
Meng-jiao DENG, Xin XU, Ying-ying MA, et al. Multi-layer Perceptron Combined with Radiative Transfer Model for Complex Land Surface Cloud Detection[J]. Acta Electronica Sinica, 2022, 50(4): 932-942.
参数名 | 取值范围 | 单位 |
---|---|---|
太阳天顶角 | 0~90 | (°) |
卫星天顶角 | 0~90 | (°) |
相对方位角 | 0~180 | (°) |
气溶胶光学厚度 | 0~3 | |
云光学厚度 | 1~50 | |
云滴有效半径 | 5~40 | μm |
下垫面高程 | 0~5 | km |
表1 关键IOPs参数设置
参数名 | 取值范围 | 单位 |
---|---|---|
太阳天顶角 | 0~90 | (°) |
卫星天顶角 | 0~90 | (°) |
相对方位角 | 0~180 | (°) |
气溶胶光学厚度 | 0~3 | |
云光学厚度 | 1~50 | |
云滴有效半径 | 5~40 | μm |
下垫面高程 | 0~5 | km |
波段编号 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 19 |
---|---|---|---|---|---|---|---|---|
中心波长/nm 波段宽度/nm 空间分辨率/m | 470 50 250 | 550 50 250 | 650 50 250 | 865 50 250 | 1380 20/30 1000 | 1640 50 1000 | 2130 50 1000 | 1030 20 1000 |
表2 MERSI II 传感器1-7及19波段基础参数
波段编号 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 19 |
---|---|---|---|---|---|---|---|---|
中心波长/nm 波段宽度/nm 空间分辨率/m | 470 50 250 | 550 50 250 | 650 50 250 | 865 50 250 | 1380 20/30 1000 | 1640 50 1000 | 2130 50 1000 | 1030 20 1000 |
数据 | 数据用处 | 下载链接 |
---|---|---|
MCD43C1和MCD12C1产品 | 辐射传输模型模拟的输入参数 | https://ladsweb.modaps.eosdis.nasa.gov/ |
MERSI II真实观测数据 | 多层感知机模型的测试数据样本 | http://www.nsmc.org.cn/nsmc/cn/home/index.html |
VFM产品 | 多层感知机模型的测试数据标签 | https://eosweb.larc.nasa.gov/ |
表3 所用数据
数据 | 数据用处 | 下载链接 |
---|---|---|
MCD43C1和MCD12C1产品 | 辐射传输模型模拟的输入参数 | https://ladsweb.modaps.eosdis.nasa.gov/ |
MERSI II真实观测数据 | 多层感知机模型的测试数据样本 | http://www.nsmc.org.cn/nsmc/cn/home/index.html |
VFM产品 | 多层感知机模型的测试数据标签 | https://eosweb.larc.nasa.gov/ |
地表类型 | 多层感知机 | MYD35产品 | ||
---|---|---|---|---|
正确率/% | 样本数 | 正确率/% | 样本数 | |
常绿针叶林 | 82.22 | 48 575 | 81.86 | 26 725 |
常绿阔叶林 | 87.89 | 189 812 | 85.32 | 119 952 |
落叶针叶林 | 75.14 | 23 484 | 76.56 | 11 749 |
落叶阔叶林 | 78.14 | 146 206 | 79.07 | 84 908 |
混合森林 | 81.15 | 300 831 | 80.28 | 168 210 |
浓密灌木丛 | 77.32 | 1 036 | 91.76 | 1 141 |
稀疏灌木丛 | 79.23 | 39 085 | 78.41 | 43 289 |
热带稀树草原 | 78.79 | 465 934 | 79.78 | 273 813 |
热带草原 | 80.35 | 318 991 | 80.66 | 189 121 |
草地 | 71.44 | 1 058 269 | 75.97 | 676 581 |
永久性湿地 | 83.38 | 10 911 | 83.19 | 8 376 |
农田 | 77.31 | 632 714 | 78.69 | 420 477 |
城镇用地 | 83.37 | 21 165 | 83.25 | 15 016 |
天然植被和农田 | 84.52 | 46 529 | 83.31 | 32 477 |
冰雪地 | 75.09 | 5 901 | 80.54 | 5 606 |
裸土或稀疏植被 | 73.11 | 553 863 | 72.66 | 369 922 |
表4 不同地表类型下的云检测正确率比较
地表类型 | 多层感知机 | MYD35产品 | ||
---|---|---|---|---|
正确率/% | 样本数 | 正确率/% | 样本数 | |
常绿针叶林 | 82.22 | 48 575 | 81.86 | 26 725 |
常绿阔叶林 | 87.89 | 189 812 | 85.32 | 119 952 |
落叶针叶林 | 75.14 | 23 484 | 76.56 | 11 749 |
落叶阔叶林 | 78.14 | 146 206 | 79.07 | 84 908 |
混合森林 | 81.15 | 300 831 | 80.28 | 168 210 |
浓密灌木丛 | 77.32 | 1 036 | 91.76 | 1 141 |
稀疏灌木丛 | 79.23 | 39 085 | 78.41 | 43 289 |
热带稀树草原 | 78.79 | 465 934 | 79.78 | 273 813 |
热带草原 | 80.35 | 318 991 | 80.66 | 189 121 |
草地 | 71.44 | 1 058 269 | 75.97 | 676 581 |
永久性湿地 | 83.38 | 10 911 | 83.19 | 8 376 |
农田 | 77.31 | 632 714 | 78.69 | 420 477 |
城镇用地 | 83.37 | 21 165 | 83.25 | 15 016 |
天然植被和农田 | 84.52 | 46 529 | 83.31 | 32 477 |
冰雪地 | 75.09 | 5 901 | 80.54 | 5 606 |
裸土或稀疏植被 | 73.11 | 553 863 | 72.66 | 369 922 |
方法 | 正确率/% | F1/% |
---|---|---|
RF | 69.51 | 79.27 |
NB | 73.39 | 79.46 |
KNN | 74.27 | 79.28 |
MLP | 76.25 | 82.32 |
表5 4种方法的对比
方法 | 正确率/% | F1/% |
---|---|---|
RF | 69.51 | 79.27 |
NB | 73.39 | 79.46 |
KNN | 74.27 | 79.28 |
MLP | 76.25 | 82.32 |
1 | ZHUZ, WOODCOCKC E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118: 83-94. |
2 | JINS, ZHANGM, MAY, et al. Adapting the dark target algorithm to advanced MERSI sensor on the FengYun-3-D satellite: Retrieval and Validation of Aerosol Optical Depth Over Land[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8781-8797. |
3 | 胡根生, 查慧敏, 梁栋, 等. 结合分类与迁移学习的薄云覆盖遥感图像地物信息恢复[J]. 电子学报, 2017, 45(12): 2855-2862. |
HUG S, ZHAH M, LIANGD, et al. Ground object information recovery for thin cloud contaminated remote sensing images by combining classification with transfer learning[J]. Acta Electronica Sinica, 2017, 45(12): 2855-2862.(in Chinese) | |
4 | JEDLOVECG J, HAINESS L, LAFONTAINEF J. Spatial and temporal varying thresholds for cloud detection in GOES imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1705-1717. |
5 | HAGOLLEO, HUC M, PASCUALD V, et al. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images[J]. Remote Sensing of Environment, 2010, 114(8): 1747-1755. |
6 | SAUNDERSR W, KRIEBELK T. An improved method for detecting clear sky and cloudy radiances from AVHRR data[J]. International Journal of Remote Sensing, 1988, 9(1): 123-150. |
7 | KRIEBELK-T, GESELLG, STNERM KA, et al. The cloud analysis tool APOLLO: Improvements and validations[J]. International Journal of Remote Sensing, 2003, 24(12): 2389-2408. |
8 | WEIJ, HUANGW, LIZ Q, et al. Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches[J]. Remote Sensing of Environment, 2020, 248: 1-14. |
9 | LIY, CHENW, ZHANGY, et al. Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning[J]. Remote Sensing of Environment, 2020, 250: 1-18. |
10 | YANGZ, ZHANGP, GUS, et al. Capability of Fengyun-3D satellite in earth system observation[J]. Journal of Meteorological Research, 2019, 33(6): 1113-1130. |
11 | ROSSOWW B, GARDERL C. Cloud detection using satellite measurements of infrared and visible radiances for ISCCP[J]. Journal of Climate, 1993, 6(12): 2341-2369. |
12 | IRISHR R. Landsat 7 automatic cloud cover assessment[C]//Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Orlando: International Society for Optics and Photonics, 2000: 348-355. |
13 | IRISHR R, BARKERJ L, GOWARDS N, et al. Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(10): 1179-1188. |
14 | ACKERMANS A, STRABALAK I, MENZELW P, et al. Discriminating clear sky from clouds with MODIS[J]. Journal of Geophysical Research: Atmospheres, 1998, 103(D24): 32141-32157. |
15 | SUNL, WEIJ, WANGJ, et al. A universal dynamic threshold cloud detection algorithm(UDTCDA) supported by a prior surface reflectance database[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(12): 7172-7196. |
16 | VERMOTEE F, TANRÉD, DEUZEJ L, et al. Second simulation of the satellite signal in the solar spectrum, 6S: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675-686. |
17 | VERMOTEE, TANRÉD, DEUZÉJ L, et al. Second simulation of a satellite signal in the solar spectrum-vector (6SV)[J]. 6S User Guide Version, 2006, 3(2): 1-55. |
18 | WANGH, HEY, GUANH. Application of support vector machines in cloud detection using EOS/MODIS[C]//Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support. San Diego: International Society for Optics and Photonics, 2008: 1-8. |
19 | MENGF, YANGX, ZHOUC, et al. A sparse dictionary learning-based adaptive patch inpainting method for thick clouds removal from high-spatial resolution remote sensing imagery[J]. Sensors, 2017, 17(9): 1-16. |
20 | 徐少平, 林珍玉, 张贵珍, 等. 采用深度学习与图像融合混合实现策略的低照度图像增强算法[J]. 电子学报, 2021, 49(1): 72-76. |
XUS P, LINZ Y, ZHANGG Z, et al. A low-light image enhancement algorithm using the hybrid strategy of deep learning and image fusion[J]. Acta Electronica Sinica, 2021, 49(1): 72-76. (in Chinese) | |
21 | 周涛, 霍兵强, 陆惠玲, 等. 残差神经网络及其在医学图像处理中的应用研究[J]. 电子学报, 2020, 48(7): 1436-1447. |
ZHOUT, HUOB Q, LUH L, et al. Research on residual neural network and its application on medical image processing[J]. Acta Electronica Sinica, 2020, 48(7): 1436-1447. (in Chinese) | |
22 | 刘启超, 肖亮, 刘芳, 等. SSCDenseNet: 一种空-谱卷积稠密网络的高光谱图像分类算法[J]. 电子学报, 2020, 48(4): 751-762. |
LIUQ C, XIAOL, LIUF, et al. SSCDenseNet: A spectral-spatial convolutional dense network for hyperspectral image classification[J]. Acta Electronica Sinica, 2020, 48(4): 751-762. (in Chinese) | |
23 | XIEF, SHIM, SHIZ, et al. Multilevel cloud detection in remote sensing images based on deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3631-3640. |
24 | SEGAL-ROZENHAIMERM, LIA, DAS K, et al. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks(CNN)[J]. Remote Sensing of Environment, 2020, 237: 1-17. |
25 | LONGJ, SHELHAMERE, DARRELLT. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440. |
26 | CHAID, NEWSAMS, ZHANGH K, et al. Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks[J]. Remote Sensing of Environment, 2019, 225: 307-316. |
27 | MATEO-GARCÍAG, LAPARRAV, LÓPEZ-PUIGDOLLERSD, et al. Transferring deep learning models for cloud detection between Landsat-8 and Proba-V[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160: 1-17. |
28 | LIZ, SHENH, CHENGQ, et al. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 197-212. |
29 | WIELANDM, LIY, MARTINISS. Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network[J]. Remote Sensing of Environment, 2019, 230: 1-12. |
30 | YANGJ, GUOJ, YUEH, et al. CDnet: CNN-based cloud detection for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 6195-6211. |
31 | LUOTAMOM, METSÄMÄKIS, KLAMIA. Multiscale cloud detection in remote sensing images using a dual convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(6): 4972-4983. |
32 | RICCHIAZZIP, YANGS, GAUTIERC, et al. SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth's atmosphere[J]. Bulletin of the American Meteorological Society, 1998, 79(10): 2101-2114. |
33 | STAMNESK, HAMREB, STAMNESJ J, et al. Modeling of radiation transport in coupled atmosphere-snow-ice-ocean systems[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011, 112(4): 714-726. |
34 | ATKINSONP M, TATNALLA R L. Introduction neural networks in remote sensing[J]. International Journal of Remote Sensing, 1997, 18(4): 699-709. |
35 | HET, DONGZ, MENGK, et al. Accelerating multi-layer perceptron based short term demand forecasting using graphics processing units[C]//Proceedings of the 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific. Seoul: IEEE, 2009: 1-4. |
36 | 任进军, 王宁. 人工神经网络中损失函数的研究[J]. 甘肃高师学报, 2018, 23(2): 61-63. |
RENJ J, WANGN. Research on cost function in artificial neural network[J]. Journal of Gansu Normal Colleges, 2018, 23(2): 61-63. (in Chinese) | |
37 | XUN, NIUX, HUX, et al. Prelaunch calibration and radiometric performance of the advanced MERSI II on FengYun-3D[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4866-4875. |
38 | SALOMONSONV V, BARNESW, MASUOKAE J. Introduction to MODIS and An Overview of Associated Activities[M]. Berlin: Springer, 2006: 12-32. |
39 | GAOB C, GOETZA F H, WISCOMBEW J. Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 μm water vapor band[J]. Geophysical Research Letters, 1993, 20(4): 301-304. |
40 | GUOG, WANGH, BELLD, et al. KNN model-based approach in classification[C]//On The Move to Meaningful Internet Systems Confederated International Conferences. Berlin: Springer, 2003: 986-996. |
41 | SUCARL E. Probabilistic Graphical Models[M]. Berlin: Springer, 2021: 43-69. |
42 | PARMARA, KATARIYAR, PATELV. A review on random forest: An ensemble classifier[C]//International Conference on Intelligent Data Communication Technologies and Internet of Things. Coimbatore: Springer, 2018: 1-6. |
43 | ZHUY, MAY, LIUB, et al. Retrieving the vertical distribution of PM2.5 mass concentration from lidar via a random forest Model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-9. |
44 | 陈喆, 贾春福, 宗楠, 等. 随机森林在程序分支混淆中的应用[J]. 电子学报, 2018, 46(10): 2458-2466. |
CHENZ, JINC F, ZONGN, et al. Branch obfuscation using random forest[J]. Acta Electronica Sinica, 2018, 46(10): 2458-2466. (in Chinese) |
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