Mapping cropland at metric resolution using the spatiotemporal information from multi-source GF satellite data

Zhiwen CAI; Zhen HE; Wenjing WANG; Jingya YANG; Haodong WEI; Cong WANG; Baodong XU

doi:10.11834/jrs.20221069

Agricultural Remote Sensing | Views : 0 下载量: 1488 CSCD: 9

R-PDF
PDF
Export
Share
Collection
Album

Mapping cropland at metric resolution using the spatiotemporal information from multi-source GF satellite data
Vol. 26, Issue 7, Pages: 1368-1382(2022)
Received：08 February 2021，

Published：07 July 2022
DOI： 10.11834/jrs.20221069
稿件说明：

移动端阅览

蔡志文，何真，王文静，杨靖雅，魏浩东，王聪，徐保东.2022.基于多源国产高分卫星时空信息的米级分辨率耕地提取.遥感学报，26（7）： 1368-1382 DOI： 10.11834/jrs.20221069.

Cai Z W，He Z，Wang W J，Yang J Y，Wei H D，Wang C and Xu B D. 2022. Mapping cropland at metric resolution using the spatiotemporal information from multi-source GF satellite data. National Remote Sensing Bulletin， 26（7）：1368-1382 DOI： 10.11834/jrs.20221069.

摘要

及时准确地获取耕地空间分布数据对于农业生产管理、产量估算、种植结构调整等具有重要意义。目前的耕地提取多基于多时相中低分辨率影像或单时相高分辨率影像，难以满足耕地破碎，农作物种植模式复杂的区域精度需求。基于此，本研究通过协同国产高分一号（GF-1）、高分二号（GF-2）和高分六号（GF-6）卫星影像，探索米级分辨率尺度下的耕地高精度提取方法。该方法以深度神经网络UNet为基础，通过协同GF-1/6的多时相优势和GF-2影像的高空间分辨率构建了CEUNet（Cropland Extraction UNet）模型，以充分挖掘耕地的时相特征和空间几何特征。同时，将基于CEUNet模型提取的米级耕地结果分别与基于UNet和多源不同分辨率遥感影像的语义分割（UNet_m）、基于UNet和单时相高分辨率影像的语义分割（UNet_s）、基于对象的随机森林分类（OBIA）、基于像元的随机森林分类（RF）提取的耕地结果展开对比，分析所提出的方法在不同区域的适宜性。结果表明，基于CEUNet模型提取的米级耕地总体精度达到92.92%，且基于CEUNet提取的耕地的逐像元验证结果在平均F1-Score值上相较于基于对象和基于像元的随机森林分类分别提升了0.21和0.21，相较于UNet_m和UNet_s分别提升了0.04和0.11，其中针对地块破碎，景观异质性高等区域，CEUNet相较于UNet_m和UNet_s提升了0.09和0.26。本研究提出的CEUNet模型能够充分发挥多源国产高分卫星数据的空间和时间优势，两者结合能够快速、高效地提取不同农业景观及不同种植模式的耕地空间分布信息。

Abstract

Timely and accurate estimation of the spatial distribution of cropland is critical for agricultural production management

yield estimation

and planting structure adjustment. Previous studies on cropland mapping mostly focused on using moderate-/low-spatial-resolution images or single-phase high-spatial-resolution images

in which croplands in regions with fragmented landscapes and complex crop planting patterns are challenging to extract. The multi-source Gaofen (GF) satellites launched by China can provide images with high spatiotemporal resolution

thus presenting great potential for fine-scale cropland mapping with high accuracy. This study utilized GF-1

GF-2

and GF-6 satellites to explore a high-accuracy cropland mapping method at metric spatial resolution. Specifically

Cropland Extraction UNet (CEUNet) was developed based on the structure of UNet by integrating multi-temporal information from GF-1/6 and spatial details from GF-2 to fully exploit the spatial and temporal characteristics of cropland.

To make full use of the details provided by high-spatial-resolution images

CEUNet adopted the same encoder structure as UNet. It consisted of the repeated application of two 3×3 convolutions (unpadded convolutions)

each followed by a 2×2 max pooling operation with stride of 2 for down sampling. The input image size was gradually reduced at each down sampling step

and feature maps of each size were concatenated in the corresponding up-sample layer to extract the hierarchy of spatial information. Meanwhile

time-series feature maps of images with moderate to high spatial resolution were extracted by two consecutive 3×3 convolutional layers and a 1×1 convolutional layer. Then

the time-series and spatial feature maps were integrated via element-wise addition before they were sent to the decoder for pixel-wise classification.

Evaluation results from randomly selected sample points showed that CEUNet achieved good performance with an overall accuracy of 92.92% over the whole of Qianjiang City

Hubei Province. The CEUNet-extracted cropland at the meter-level resolution was used to perform a wall-to-wall pixel comparison with the cropland extracted via semantic segmentation based on UNet by using multi-source remote sensing images with different resolutions (UNet_m). Semantic segmentation based on UNet using single-phase high-resolution images (UNet_s)

object-based random forest classification (OBIA)

and pixel-based random forest classification (RF) were employed to extract the results of cultivated land for comparison. The accuracy of cropland extraction by CEUNet was higher than that by others (the average F1 score was improved by about 0.04

0.11

0.21

and 0.21)

indicating the effectiveness of the proposed approach for diverse agriculture landscapes. The F1 score of CEUNet was improved by about 0.09

0.26

0.27

and 0.27 over regions with high fragmentation and complex landscapes.

关键词

Keywords

references

Baheti B , Innani S , Gajre S and Talbar S . 2020 . Eff-UNet: a novel architecture for semantic segmentation in unstructured environment // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Seattle, WA, USA IEEE : 1473 - 1481 [ DOI: 10.1109/CVPRW50498.2020.00187 http://dx.doi.org/10.1109/CVPRW50498.2020.00187 ]

Bruzzone L and Carlin L . 2006 . A multilevel context-based system for classification of very high spatial resolution images . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 9 ): 2587 - 2600 [ DOI: 10.1109/tgrs.2006.875360 http://dx.doi.org/10.1109/tgrs.2006.875360 ]

Cao K L and Zhang X L . 2020 . An improved res-UNet model for tree species classification using airborne high-resolution images . Remote Sensing , 12 ( 7 ): 1128 [ DOI: 10.3390/rs12071128 http://dx.doi.org/10.3390/rs12071128 ]

Chen K Q , Gao X , Yan M L , Zhang Y and Sun X . 2020 . Building extraction in pixel level from aerial imagery with a deep encoder-decoder network . Journal of Remote Sensing , 24 ( 9 ): 1134 - 1142

陈凯强 , 高鑫 , 闫梦龙 , 张跃 , 孙显 . 2020 . 基于编解码网络的航空影像像素级建筑物提取 . 遥感学报 , 24 ( 9 ): 1134 - 1142 [ DOI: 10.11834/jrs.20209056 http://dx.doi.org/10.11834/jrs.20209056 ]

Du Z R , Yang J Y , Ou C and Zhang T T . 2019 . Smallholder crop area mapped with a semantic segmentation deep learning method . Remote Sensing , 11 ( 7 ): 888 [ DOI: 10.3390/rs11070888 http://dx.doi.org/10.3390/rs11070888 ]

Fritz S , See L , McCallum I , You L Z , Bun A , Moltchanova E , Duerauer M , Albrecht F , Schill C , Perger C , Havlik P , Mosnier A , Thornton P , Wood-Sichra U , Herrero M , Becker-Reshef I , Justice C , Hansen M , Gong P , Aziz S A , Cipriani A , Cumani R , Cecchi G , Conchedda G , Ferreira S , Gomez A , Haffani M , Kayitakire F , Malanding J , Mueller R , Newby T , Nonguierma A , Olusegun A , Ortner S , Rajak D R , Rocha J , Schepaschenko D , Schepaschenko M , Terekhov A , Tiangwa A , Vancutsem C , Vintrou E , Wu W B , Van Der Velde M , Dunwoody A , Kraxner F and Obersteiner M . 2015 . Mapping global cropland and field size . Global Change Biology , 21 ( 5 ): 1980 - 1992 [ DOI: 10.1111/gcb.12838 http://dx.doi.org/10.1111/gcb.12838 ]

Geiger F , Bengtsson J , Berendse F , Weisser W W , Emmerson M , Morales M B , Ceryngier P , Liira J , Tscharntke T , Winqvist C , Eggers S , Bommarco R , Pärt T , Bretagnolle V , Plantegenest M , Clement L W , Dennis C , Palmer C , Oñate J J , Guerrero I , Hawro V , Aavik T , Thies C , Flohre A , Hänke S , Fischer C , Goedhart P W and Inchausti P . 2010 . Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland . Basic and Applied Ecology , 11 ( 2 ): 97 - 105 [ DOI: 10.1016/j.baae.2009.12.001 http://dx.doi.org/10.1016/j.baae.2009.12.001 ]

Godfray H C J , Beddington J R , Crute I R , Haddad L , Lawrence D , Muir J F , Pretty J , Robinson S , Thomas S M and Toulmin C . 2010 . Food security: the challenge of feeding 9 billion people . Science , 327 ( 5967 ): 812 - 818 [ DOI: 10.1126/science.1185383 http://dx.doi.org/10.1126/science.1185383 ]

Graesser J and Ramankutty N . 2017 . Detection of cropland field parcels from Landsat imagery . Remote Sensing of Environment , 201 : 165 - 180 [ DOI: 10.1016/j.rse.2017.08.027 http://dx.doi.org/10.1016/j.rse.2017.08.027 ]

Green R E , Cornell S J , Scharlemann J P W and Balmford A . 2005 . Farming and the fate of wild nature . Science , 307 ( 5709 ): 550 - 555 [ DOI: 10.1126/science.1106049 http://dx.doi.org/10.1126/science.1106049 ]

He M and Zhang J Y . 2005 . Scientific research progress of global land-use and land-cover changes project . Land and Resources , ( 9 ): 22 - 25 .

何蔓 , 张军岩 . 2005 . 全球土地利用与覆盖变化(LUCC)研究及其进展 . 国土资源 , ( 9 ): 22 - 25

Kehoe L , Romero-Muñoz A , Polaina E , Estes L , Kreft H and Kuemmerle T . 2017 . Biodiversity at risk under future cropland expansion and intensification . Nature Ecology and Evolution , 1 ( 8 ): 1129 - 1135 [ DOI: 10.1038/s41559-017-0234-3 http://dx.doi.org/10.1038/s41559-017-0234-3 ]

Kingma D P and Ba J . 2014 . Adam: a method for stochastic optimization . arXiv : 1412 . 6980 [ DOI: 10.48550/arXiv.1412.6980 http://dx.doi.org/10.48550/arXiv.1412.6980 ]

Kussul N , Lavreniuk M , Skakun S and Shelestov A . 2017 . Deep learning classification of land cover and crop types using remote sensing data . IEEE Geoscience and Remote Sensing Letters , 14 ( 5 ): 778 - 782 [ DOI: 10.1109/lgrs.2017.2681128 http://dx.doi.org/10.1109/lgrs.2017.2681128 ]

Li W J , Dong R M , Fu H H , Wang J , Yu L and Gong P . 2020 . Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping . Remote Sensing of Environment , 237 : 111563 . [ DOI: 10.1016/j.rse.2019.111563 http://dx.doi.org/10.1016/j.rse.2019.111563 ]

Lin T Y , Goyal P , Girshick R , He K M and Dollár P . 2017 . Focal loss for dense object detection // Proceedings of the IEEE International Conference on Computer Vision . Venice, Italy : IEEE: 2999 - 3007 . [ DOI: 10.1109/ICCV.2017.324 http://dx.doi.org/10.1109/ICCV.2017.324 ]

Lowder S K , Skoet J and Raney T . 2016 . The number, size and distribution of farms, smallholder farms and family farms worldwide . World Development , 87 : 16 - 29 . [ DOI: 10.1016/j.worlddev.2015.10.041 http://dx.doi.org/10.1016/j.worlddev.2015.10.041 ]

Milletari F , Navab N and Ahmadi S A . 2016 . V-Net: fully convolutional neural networks for volumetric medical image segmentation // 2016 Fourth International Conference on 3D Vision (3DV) . Stanford, CA, USA : IEEE: 565 - 571 [ DOI: 10.1109/3dv.2016.79 http://dx.doi.org/10.1109/3dv.2016.79 ]

Ming D P , Ci T Y , Cai H Y , Li L X , Qiao C and Du J Y . 2012 . Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm . IEEE Geoscience and Remote Sensing Letters , 9 ( 5 ): 813 - 817 [ DOI: 10.1109/lgrs.2011.2182604 http://dx.doi.org/10.1109/lgrs.2011.2182604 ]

Ming D P , Li J , Wang J Y and Zhang M . 2015 . Scale parameter selection by spatial statistics for GeOBIA: using mean-shift based multi-scale segmentation as an example . ISPRS Journal of Photogrammetry and Remote Sensing , 106 : 28 - 41 [ DOI: 10.1016/j.isprsjprs.2015.04.010 http://dx.doi.org/10.1016/j.isprsjprs.2015.04.010 ]

Ming D P , Zhang X , Wang M and Zhou W . 2016 . Cropland extraction based on OBIA and adaptive scale pre-estimation . Photogrammetric Engineering and Remote Sensing , 82 ( 8 ): 635 - 644 [ DOI: 10.14358/pers.82.8.635 http://dx.doi.org/10.14358/pers.82.8.635 ]

Persello C , Tolpekin V A , Bergado J R and De By R A . 2019 . Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping . Remote Sensing of Environment , 231 : 111253 [ DOI: 10.1016/j.rse.2019.111253 http://dx.doi.org/10.1016/j.rse.2019.111253 ]

Pittman K , Hansen M C , Becker-Reshef I , Potapov P V and Justice C O . 2010 . Estimating global cropland extent with multi-year MODIS data . Remote Sensing , 2 ( 7 ): 1844 - 1863 [ DOI: 10.3390/rs2071844 http://dx.doi.org/10.3390/rs2071844 ]

Qin X F , Wu C Z , Chang H , Lu H and Zhang X D . 2020 . Match feature u-net: dynamic receptive field networks for biomedical image segmentation . Symmetry , 12 ( 8 ): 1230 . [ DOI: 10.3390/sym12081230 http://dx.doi.org/10.3390/sym12081230 ]

Rounsevell M D A , Ewert F , Reginster I , Leemans R and Carter T R . 2005 . Future scenarios of European agricultural land use: II . Projecting changes in cropland and grassland. Agriculture, Ecosystems and Environment , 107 ( 2/3 ): 117 - 135 [ DOI: 10.1016/j.agee.2004.12.002 http://dx.doi.org/10.1016/j.agee.2004.12.002 ]

Song D J , Zhang C M , Yang X X , Li F , Han Y J , Gao S and Dong H Y . 2020 . Extracting winter wheat spatial distribution information from GF-2 image . Journal of Remote Sensing , 24 ( 5 ): 596 - 608

宋德娟 , 张承明 , 杨晓霞 , 李峰 , 韩颖娟 , 高帅 , 董海燕 . 2020 . 高分二号遥感影像提取冬小麦空间分布 . 遥感学报 , 24 ( 5 ): 596 - 608 [ DOI: 10.11834/jrs.20208285 http://dx.doi.org/10.11834/jrs.20208285 ]

Su T F , Li H Y , Zhang S W and Li Y X . 2015 . Image segmentation using mean shift for extracting croplands from high-resolution remote sensing imagery . Remote Sensing Letters , 6 ( 12 ): 952 - 961 [ DOI: 10.1080/2150704x.2015.1093188 http://dx.doi.org/10.1080/2150704x.2015.1093188 ]

Thenkabail P S and Wu Z T . 2012 . An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS and secondary data . Remote Sensing , 4 ( 10 ): 2890 - 2918 [ DOI: 10.3390/rs4102890 http://dx.doi.org/10.3390/rs4102890 ]

Waldner F , Canto G S and Defourny P . 2015 . Automated annual cropland mapping using knowledge-based temporal features . ISPRS Journal of Photogrammetry and Remote Sensing , 110 : 1 - 13 [ DOI: 10.1016/j.isprsjprs.2015.09.013 http://dx.doi.org/10.1016/j.isprsjprs.2015.09.013 ]

Waldner F and Diakogiannis F I . 2020 . Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network . Remote Sensing of Environment , 245 : 111741 [ DOI: 10.1016/j.rse.2020.111741 http://dx.doi.org/10.1016/j.rse.2020.111741 ]

Watkins B and Van Niekerk A . 2019 . Automating field boundary delineation with multi-temporal Sentinel-2 imagery . Computers and Electronics in Agriculture , 167 : 105078 [ DOI: 10.1016/j.compag.2019.105078 http://dx.doi.org/10.1016/j.compag.2019.105078 ]

White E V and Roy D P . 2015 . A contemporary decennial examination of changing agricultural field sizes using Landsat time series data . Geo : Geography and Environment , 2 ( 1 ): 33 - 54 [ DOI: 10.1002/geo2.4 http://dx.doi.org/10.1002/geo2.4 ]

Xia L G , Luo J C , Sun Y W and Yang H P . 2018 . Deep extraction of cropland parcels from very high-resolution remotely sensed imagery // 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) . Hangzhou, China : IEEE: 1 - 5 [ DOI: 10.1109/Agro-Geoinformatics.2018.8476002 http://dx.doi.org/10.1109/Agro-Geoinformatics.2018.8476002 ]

Xie Y H , Lark T J , Brown J F and Gibbs H K . 2019 . Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine . ISPRS Journal of Photogrammetry and Remote Sensing , 155 : 136 - 149 [ DOI: 10.1016/j.isprsjprs.2019.07.005 http://dx.doi.org/10.1016/j.isprsjprs.2019.07.005 ]

Xiong J , Thenkabail P S , Tilton J C , Gumma M K , Teluguntla P , Oliphant A , Congalton R G , Yadav K and Gorelick N . 2017a . Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using sentinel-2 and landsat-8 data on Google earth engine . Remote Sensing , 9 ( 10 ): 1065 [ DOI: 10.3390/rs9101065 http://dx.doi.org/10.3390/rs9101065 ]

Xiong J , Thenkabail P S , Gumma M K , Teluguntla P , Poehnelt J , Congalton R G , Yadav K and Thau D . 2017b . Automated cropland mapping of continental Africa using Google Earth Engine cloud computing . ISPRS Journal of Photogrammetry and Remote Sensing , 126 : 225 - 244 [ DOI: 10.1016/j.isprsjprs.2017.01.019 http://dx.doi.org/10.1016/j.isprsjprs.2017.01.019 ]

Xu J F , Zhang B M , Yu D H , Lin Y Z and Guo H T . 2020 . Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion . Journal of Remote Sensing , 24 ( 1 ): 37 - 52

徐俊峰 , 张保明 , 余东行 , 林雨准 , 郭海涛 . 2020 . 多特征融合的高分辨率遥感影像飞机目标变化检测 . 遥感学报 , 24 ( 1 ): 37 - 52 [ DOI: 10.11834/jrs.20208213 http://dx.doi.org/10.11834/jrs.20208213 ]

Xu J F , Zhu Y , Zhong R H , Lin Z X , Xu J L , Jiang H , Huang J F , Li H F and Lin T . 2020 . DeepCropMapping: a multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping . Remote Sensing of Environment , 247 : 111946 [ DOI: 10.1016/j.rse.2020.111946 http://dx.doi.org/10.1016/j.rse.2020.111946 ]

Xu L , Ming D P , Zhou W , Bao H Q , Chen Y Y and Ling X . 2019 . Farmland extraction from high spatial resolution remote sensing images based on stratified scale pre-estimation . Remote Sensing , 11 ( 2 ): 108 [ DOI: 10.3390/rs11020108 http://dx.doi.org/10.3390/rs11020108 ]

Yang J W , Zhang J S , Zhu S , Xu Q , Zhang F , Yang Z J and Dong Z . 2019 . Cropland mapping in fragmented agricultural landscape using modified pyramid scene parsing network //IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan : IEEE: 9831 - 9834 [ DOI: 10.1109/IGARSS.2019.8897867 http://dx.doi.org/10.1109/IGARSS.2019.8897867 ]

Zhang D J , Pan Y Z , Zhang J S , Hu T G , Zhao J H , Li N and Chen Q . 2020a . A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution . Remote Sensing of Environment , 247 : 111912 [ DOI: 10.1016/j.rse.2020.111912 http://dx.doi.org/10.1016/j.rse.2020.111912 ]

Zhang J , Xie T J , Yang C H , Song H B , Jiang Z , Zhou G S , Zhang D Y , Feng H and Xie J . 2020b . Segmenting purple rapeseed leaves in the field from UAV RGB imagery using deep learning as an auxiliary means for nitrogen stress detection . Remote Sensing , 12 ( 9 ): 1403 [ DOI: 10.3390/rs12091403 http://dx.doi.org/10.3390/rs12091403 ]

Alert me when the article has been cited

提交

Reconstruction of full-polarimetric SAR data based on dual-polarimetric observations from the LT-1 Satellite

Scattering feature dimension reduction of multitemporal fully PolSAR image based on Stacked Sparse AutoEncoder

CNN with local binary patterns for hyperspectral images classification