浏览全部资源
扫码关注微信
Published： 07 July 2022 ，
扫 描 看 全 文
张乾坤，蒙继华，任超.2022.构建地块二维表征及CNN模型的作物遥感分类.遥感学报，26（7）： 1437-1449
Zhang Q K，Meng J H and Ren C. 2022. Crop classification based on two-dimensional representation and CNN model from remote sensing. National Remote Sensing Bulletin， 26（7）：1437-1449
本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法，以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像，利用其质量评定波段完成研究区无云时相及区域上的地块统计，提取地块级别的各波段反射率均值按照时相顺序及波长进行排列，构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN（Convolutional Neural Network）结构完成对特征图的分类，从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求，可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较，获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力，应视为基于地块的多时相影像分类任务的优选方法。
Crop planting area and its spatiotemporal distribution information are crucial for agricultural management
structural adjustment of the planting industry
national food security
and other fields. Remote sensing can extract crop planting information quickly. However
a large amount of ground survey information
expert knowledge
and manual correction operation after classification are needed to meet actual production needs. Using remote sensing and existing geographic information data for intelligent information extraction is the future development trend. The purpose of this study is to investigate the classification representation method of the deep learning model on the basis of the constraints of field parcel data. Multi-source heterogeneous data are linked by constructing representation. The method can identify the spectral differences of different crops in different phases and classify the crop types.
The study area is in the eighth division of Xinjiang Production and Construction Corp. The platform of Google Earth is used to obtain all Landsat 8 images during the crop growth period of the research area in 2019. The image data and their quality assessment band on GEE are utilized to determine the statistical or estimated values of phases in the study area. Then
the average reflectivity of each band of the extracted block level is arranged in accordance with the time-phase sequence and wavelength
and a plot representation of the spectrum and time-phase two-dimensional properties is constructed. The construction of land plot representation realizes the connection of geographic information and remote sensing data and makes the application of deep learning in crop remote sensing classification possible. The completed plot representation is used to construct and train a Convolution Neural Network (CNN) model.
A step-by-step optimization process is implemented to search for the best combination of super parameters and various types of layers. Construction of a relatively optimal CNN model is obtained
and classification of the constructed feature map is carried out based on this model to complete the classification of the research area in 2019. We obtain a crop distribution map whose resolution is much higher than that of the remote sensing image. Moreover
the overall accuracy of the CNN model reaches 93.04%
and the kappa coefficient is 91.09%. The results of nine kinds of crop classification are good. After thousands of rounds of data learning
the proposed method exhibits lower classification error fluctuation and higher stability than other machine learning algorithms.
The research object is plot representation
which is the abstract expression of plot planting information. It can be used as the standard input of the deep learning model. Through the construction of plot representation
crop classification can be indirectly identified by remote sensing. The proposed method has outstanding application potential in land feature representation and crop classification and should be regarded as an optimal method for multi-temporal image classification tasks based on field parcel data. The method can be used as a reference in the application of deep learning in remote sensing.
遥感作物分类CNN特征图二维表征时序影像地块数据
remote sensingcrop classificationCNNfeature mapplot representationmulti-temporal imageper-field
Anwer R M, Khan F S, Van De Weijer J, Molinier M and Laaksonen J. 2018. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 138: 74-85 [DOI: 10.1016/j.isprsjprs.2018.01.023http://dx.doi.org/10.1016/j.isprsjprs.2018.01.023]
Audebert N, Le Saux B and Lefevre S. 2017. Segment-before-detect: vehicle detection and classification through semantic segmentation of aerial images. Remote Sensing, 9(4): 368 [DOI: 10.3390/rs9040368http://dx.doi.org/10.3390/rs9040368]
Bittner K, Cui S and Reinartz P. 2017. Building extraction from remote sensing data using fully convolutional networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, XLII-1/W1: 481-486 [DOI: 10.5194/isprs-archives-XLII-1-W1-481-2017http://dx.doi.org/10.5194/isprs-archives-XLII-1-W1-481-2017]
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16 [DOI: 10.1016/j.isprsjprs.2009.06.004http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004]
Cai Y P, Guan K Y, Peng J, Wang S W, Seifert C, Wardlow B and Li Z. 2018. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment, 210: 35-47 [DOI: 10.1016/j.rse.2018.02.045http://dx.doi.org/10.1016/j.rse.2018.02.045]
Cavallaro G, Riedel M, Richerzhagen M, Benediktsson J A and Plaza A. 2015. On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10): 4634-4646 [DOI: 10.1109/JSTARS.2015.2458855http://dx.doi.org/10.1109/JSTARS.2015.2458855]
Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B and Yao Y M. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
陈仲新, 任建强, 唐华俊, 史云, 冷佩, 刘佳, 王利民, 吴文斌, 姚艳敏. 2016. 农业遥感研究应用进展与展望. 遥感学报, 20(5): 748-767 [DOI: 10.11834/jrs.20166214http://dx.doi.org/10.11834/jrs.20166214]
Guo R, Liu J B, Li N, Liu S B, Chen F, Cheng B, Duan J B, Li X P and Ma C H. 2018. Pixel-wise classification method for high resolution remote sensing imagery using deep neural networks. ISPRS International Journal of Geo-Information, 7(3): 110 [DOI: 10.3390/ijgi7030110http://dx.doi.org/10.3390/ijgi7030110]
Han X B, Zhong Y F and Zhang L P. 2016. Spatial-spectral classification based on the unsupervised convolutional sparse auto-encoder for hyperspectral remote sensing imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7: 25-31 [DOI: 10.5194/isprs-annals-III-7-25-2016http://dx.doi.org/10.5194/isprs-annals-III-7-25-2016]
Han Y X. 2018. Study on Per-Field Crop Classification Method Using Remote Sensing Data. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
韩衍欣. 2018. 面向地块的农作物遥感分类方法研究. 北京: 中国科学院遥感与数字地球研究所
Han Y X and Meng J H. 2019. A review of per-field crop classification using remote sensing. Remote Sensing for Land and Resources, 31(2): 1-9
韩衍欣, 蒙继华. 2019. 面向地块的农作物遥感分类研究进展. 国土资源遥感, 31(2): 1-9 [DOI: 10.6046/gtzyyg.2019.02.01http://dx.doi.org/10.6046/gtzyyg.2019.02.01]
Hinton G, Deng L, Yu D, Dahl G E, Mohamed A R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N and Kingsbury B. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 29(6): 82-97 [DOI: 10.1109/MSP.2012.2205597http://dx.doi.org/10.1109/MSP.2012.2205597]
Huang Q T, Qin Z L and Zeng Z K. 2016. Study on the crop classification and planting area estimation at land parcel scale using multi-sources satellite data. Journal of Geo-Information Science, 18(5): 708-717
黄启厅, 覃泽林, 曾志康. 2016. 多星数据协同的地块尺度作物分类与面积估算方法研究. 地球信息科学学报, 18(5): 708-717 [DOI: 10.3724/SP.J.1047.2016.00708http://dx.doi.org/10.3724/SP.J.1047.2016.00708]
Kingma D P and Ba J. 2014. Adam: a method for stochastic optimization//Proceedings of the 3rd International Conference on Learning Representations. San Diego: [s.n.]
LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444 [DOI: 10.1038/nature14539http://dx.doi.org/10.1038/nature14539]
Li H Y, Su A Y, Liu C, Wu Y Q and Chen S B. 2019. Bisupervised network with pyramid pooling module for land cover classification of satellite remote sensing imagery. Journal of Applied Remote Sensing, 13(4): 048502 [DOI:10.1117/1.JRS.13.048502http://dx.doi.org/10.1117/1.JRS.13.048502]
Li Z Y, Wu B F, Zhang M, Xing Q, Li M Y and Yan N N. 2019. Identifying rapeseed planting area using an object-oriented method and crop phenology. Journal of Geo-Information Science, 21(5): 720-730
李中元, 吴炳方, 张淼, 邢强, 李名勇, 闫娜娜. 2019. 利用物候差异与面向对象决策树提取油菜种植面积. 地球信息科学学报, 21(5): 720-730 [DOI: 10.12082/dqxxkx.2019.180345http://dx.doi.org/10.12082/dqxxkx.2019.180345]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 3431-3440 [DOI: 10.1109/CVPR.2015.7298965http://dx.doi.org/10.1109/CVPR.2015.7298965]
Lu C, Yang X M, Wang Z H and Li Z. 2018. Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones. International Journal of Applied Earth Observation and Geoinformation, 70: 1-12 [DOI: 10.1016/j.jag.2018.03.010http://dx.doi.org/10.1016/j.jag.2018.03.010]
Onojeghuo A O, Blackburn G A, Huang J F, Kindred D and Huang W J. 2018. Applications of satellite ‘hyper-sensing’ in Chinese agriculture: challenges and opportunities. International Journal of Applied Earth Observation and Geoinformation, 64: 62-86 [DOI: 10.1016/j.jag.2017.09.005http://dx.doi.org/10.1016/j.jag.2017.09.005]
Ping Y P. 2016. Crop Classification Based On Analysis of Phenological Characteristics of MODIS Times Serise. Harbin: Harbin Normal University
平跃鹏. 2016. 基于MODIS时间序列地表物候特征分析及农作物分类. 哈尔滨: 哈尔滨师范大学 [DOI: 10.7666/d.D831058http://dx.doi.org/10.7666/d.D831058]
Rahman M R and Saha S K. 2008. Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data. Journal of the Indian Society of Remote Sensing, 36(2): 189-201 [DOI: 10.1007/s12524-008-0020-4http://dx.doi.org/10.1007/s12524-008-0020-4]
Seal A, Garcia-Pedrero A, Bhattacharjee D, Nasipuri M, Lillo-Saavedra M, Menasalvas E and Gonzalo-Martin C. 2020. Multi-scale RoIs selection for classifying multi-spectral images. Multidimensional Systems and Signal Processing, 31(2): 745-769 [DOI: 10.1007/s11045-019-00684-1http://dx.doi.org/10.1007/s11045-019-00684-1]
Shao Z F, Yang K and Zhou W X. 2018. Performance evaluation of single-label and multi-label remote sensing image retrieval using a dense labeling dataset. Remote Sensing, 10(6): 964 [DOI: 10.3390/rs10060964http://dx.doi.org/10.3390/rs10060964]
Shi Z, Liang Z Z, Yang Y Y and Guo Y. 2015. Status and Prospect of agricultural remote sensing. Transactions of the Chinese Society for Agricultural Machinery, 46(2): 247-260
史舟, 梁宗正, 杨媛媛, 郭燕. 2015. 农业遥感研究现状与展望. 农业机械学报, 46(2): 247-260
Shin H C, Roth H R, Gao M C, Lu L, Xu Z Y, Nogues I, Yao J H, Mollura D and Summers R M. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5): 1285-1298 [DOI: 10.1109/TMI.2016.2528162http://dx.doi.org/10.1109/TMI.2016.2528162]
Su C, Feng C J, Huang Z C and Zhang X C. 2018. Extraction of rice cropping area from high resolution remote sensing image based on sample knowledge mining//Proceedings of the 2018 7th International Conference on Agro-Geoinformatics. Hangzhou: IEEE: 1-7 [DOI: 10.1109/Agro-Geoinformatics.2018.8475988http://dx.doi.org/10.1109/Agro-Geoinformatics.2018.8475988]
Sun C L, Bian Y, Zhou T and Pan J J. 2019. Using of multi-source and multi-temporal remote sensing data improves crop-type mapping in the subtropical agriculture region. Sensors, 19(10): 2401 [DOI: 10.3390/s19102401http://dx.doi.org/10.3390/s19102401]
Valero S, Morin D, Inglada J, Sepulcre G, Arias M, Hagolle O, Dedieu G, Bontemps S, Defourny P and Koetz B. 2016. Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions. Remote Sensing, 8(1): 55 [DOI: 10.3390/rs8010055http://dx.doi.org/10.3390/rs8010055]
Wang H Z, Wang Y, Zhang Q, Xiang S M and Pan C H. 2017. Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sensing, 9(5): 446 [DOI: 10.3390/rs9050446http://dx.doi.org/10.3390/rs9050446]
Wei W, Zhang J Y, Zhang L, Tian C N and Zhang Y N. 2018. Deep cube-pair network for Hyperspectral imagery classification. Remote Sensing, 10(5): 783 [DOI: 10.3390/rs10050783http://dx.doi.org/10.3390/rs10050783]
Zabalza J, Ren J C, Zheng J B, Zhao H M, Qing C, Yang Z J, Du P J and Marshall S. 2016. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, 185: 1-10 [DOI: 10.1016/j.neucom.2015.11.044http://dx.doi.org/10.1016/j.neucom.2015.11.044]
Zhang C, Pan X, Li H P, Gardiner A, Sargent I, Hare J and Atkinson P M. 2018. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 140: 133-144 [DOI: 10.1016/j.isprsjprs.2017.07.014http://dx.doi.org/10.1016/j.isprsjprs.2017.07.014]
Zhang H X, Li Q Z, Liu J G, Shang J L, Du X, Mcnairn H, Champagne C, Dong T F and Liu M X. 2017. Image classification using RapidEye data: integration of spectral and textual features in a random forest classifier. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12): 5334-5349 [DOI: 10.1109/JSTARS.2017.2774807http://dx.doi.org/10.1109/JSTARS.2017.2774807]
Zhang L F, Zhang L P, Tao D C and Huang X. 2012. On combining multiple features for Hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 879-893 [DOI: 10.1109/TGRS.2011.2162339http://dx.doi.org/10.1109/TGRS.2011.2162339]
Zhong L H, Hu L N and Zhou H. 2019. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221: 430-443 [DOI: 10.1016/j.rse.2018.11.032http://dx.doi.org/10.1016/j.rse.2018.11.032]
相关文章
相关作者
相关机构
京公网安备11010802024621