Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2019, Vol. 34 Issue (4): 694-703    DOI: 10.11873/j.issn.1004-0323.2019.4.0694
    
Crop Mapping Using Remotely Sensed Spectral and Context Features based on CNN
Zhuang Zhou1,2,3(),Shengyang Li1,2,Kang Zhang1,2,3,Yuyang Shao1,2
1. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
2. Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
Download:  HTML  PDF (4024KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Deep learning algorithms such as Convolutional Neural Network (CNN) can learn the representative and discriminative features in a hierarchical manner from the remote sensing data. Considering the low-level features as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification, the CNN has a broad application prospect in the field of agricultural remote sensing. The advantage of CNN in feature extraction can obtain the crop classification in complex planting structure area from multi-spectral remote sensing data, which is difficult in conventional methods. In this paper, a crop mapping method using remotely sensed spectral and context features based on CNN from Landsat OLI data is proposed and applied in Yuanyang county.The architecture of the proposed CNN classifier contains eight layers with weights which are the input layer, two convolution layers, two max pooling layers, two full connection layers and output layer. These eight layers are implemented on spectral and context signatures from 4 different phase Landsat OLI images to discriminate different crops against others. Experimental results demonstrate that the proposed CNN classifier can achieve better classification performance than support vector machines in spectral domain. The context features calculated by the gray level co-occurrence matrix method from Landsat OLI data can enhance the proposed CNN method to achieve the best results.In terms of verification accuracy, the proposed CNN classifier is superior than SVM in spectral domain. The overall accuracy of the two methods is 95.14% and 91.77%, respectively. The accuracy of the proposed classifier is further improved by adding spatial context features on the basis of spectral information. The overall accuracy and Kappa coefficient of the proposed method is 96.43% and 0.952.Furthermore, the crop mapping using spectral and context features based on CNN achieves better spatial representation especially for peanut and roads which is easy to form mixed-pixel. The context features can be extracted by the CNN to enhance the feature representation of these small objects.The CNN-based method from remotely sensed spectral and context features for crop mapping can achieve outstanding performance especially for the fine ground objects in complex planting structure area such as peanuts and roads.

Key words:  Crop      Remote sensing      Classification      CNN      Context features     
Received:  25 June 2018      Published:  16 October 2019
ZTFLH:  TP79  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Zhuang Zhou
Shengyang Li
Kang Zhang
Yuyang Shao

Cite this article: 

Zhuang Zhou,Shengyang Li,Kang Zhang,Yuyang Shao. Crop Mapping Using Remotely Sensed Spectral and Context Features based on CNN. Remote Sensing Technology and Application, 2019, 34(4): 694-703.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.4.0694     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I4/694

Fig.1  General situation of study area
编号时相传感器空间分辨率/m云量/%
12015年7月28日OLI301.9
22015年8月13日OLI304.7
32015年9月14日OLI300
42015年10月16日OLI300
52015年8月28日PMS0.80
Table 2  List of remote sensing images
日期07-2808-1309-1410-16
水稻分蘖抽穗成熟
玉米拔节成熟
花生开花下针成熟
Table 2  Crop calendar in Yuanyang Country
编号类别训练集验证集测试集
1水稻13345267
2玉米12542251
3花生9230183
4其他15451307
Table 3  List of sample data
Fig.2  Technical flowchart
Fig.3  Image acquisition process
Fig.4  The architecture of the proposed CNN classifier
Fig.6  Classification spatial map of study area
类别水稻玉米花生其他总计UA/%
PA/%93.4588.8486.0396.04
水稻25744226796.25
玉米722314725188.84
花生917154318384.15
其他27729130794.79
总计2752511793031 008
Table 4  Confusion matrix result of SVM
类别水稻玉米花生其他总计UA/%
PA/%95.2493.2894.1597.11
水稻26011526797.38
玉米82366125194.02
花生316161318387.98
其他20330230798.37
总计2732531713111 008
Table 5  Confusion matrix result of CNN with spectral features
类别水稻玉米花生其他总计UA/%
PA/%96.6994.8094.9498.38
水稻26301326798.50
玉米72376125194.42
花生112169118392.35
其他11230330798.70
总计2722501783081 008
Table 6  Confusion matrix result of CNN with spectral and spatial features
分类方法总体精度/%Kappa系数
SVM分类91.770.889
基于光谱的CNN分类95.140.934
基于光谱+空间的CNN分类96.430.952
Table 7  Accuracy and Kappa coefficient of classification results
Fig.5  The NIR spectral value curves of crop in difference times
Fig.7  Classification spatial local map of study area
1 TangHuajun, WuWenbin, YangPeng, et al. Recent Progresses in Monitoring Crop Spatial Patterns by Using Remote Sensing Technologies[J]. Scientia Agricultura Sinica, 2010, 43(14): 2879-2888.
1 唐华俊, 吴文斌, 杨鹏, 等. 农作物空间格局遥感监测研究进展[J]. 中国农业科学, 2010, 43(14): 2879-2888.
2 XiaoX, BolesS, FrolkingS, et al. Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-temporal MODIS Images[J]. Remote Sensing of Environment, 2006, 100(1): 95-113.
3 CaoWeibin, YangBangjie, SongJinpeng. Spectral Information based Model for Cotton Identification on Landsat TM Image[J]. Transactions of the CSAE, 2004, 20(4): 112-116.
3 曹卫彬, 杨邦杰, 宋金鹏. TM影像中基于光谱特征的棉花识别模型[J]. 农业工程学报, 2004, 20(4):112-116.
4 JiaKun, LiQiangzi, TianYichen, et al. Accuracy Improvement of Spectral Classification of Crop Using Microwave Backscatter Data[J]. Spectroscopy and Spectral Analysis, 2011, 31(2):483-487.
4 贾坤, 李强子, 田亦陈,等. 微波后向散射数据改进农作物光谱分类精度研究[J]. 光谱学与光谱分析, 2011,31(2): 483-487.
5 YangC, EverittJ H, MurdenD. Evaluating High Resolution SPOT 5 Satellite Imagery for Crop Identification[J]. Computers and Electronics in Agriculture, 2011, 75(2): 347-354.
6 LiuKebao, LiuShubin, LiZhongjun. Extraction on Cropping Structure based on High Spatial Resolution Remote Sensing Data[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2014, 35(1): 21-26.
6 刘克宝, 刘述彬, 陆忠军,等. 利用高空间分辨率遥感数据的农作物种植结构提取[J]. 中国农业资源与区划, 2014, 35(1):21-26.
7 ZhuDengsheng, PanJiazhi, HeYong. Identification Methods of Crop and Weeds based on VIs/NIR spectroscopy and RBF-NN model[J]. Spectroscopy and Spectral Analysis, 2008, 28(5): 1102-1106.
7 朱登胜, 潘家志, 何勇. 基于光谱和神经网络模型的作物与杂草识别方法研究[J]. 光谱学与光谱分析, 2008, 28(5): 1102-1106.
8 PengGuangxiong, GongAdu, CuiWeihong. Study on Methods Comparision of Typical Remote Sensing Classification based on Multi-temporal Images[J]. Journal of Geo-Information Science, 2012, 11(2): 20-26.
8 彭光雄, 宫阿都, 崔伟宏, 等. 多时相影像的典型区农作物识别分类方法对比研究[J]. 地球信息科学学报, 2012, 11(2): 225-230.
9 XiongQinxue, HuangJingfeng. Estimation of Autumn Harvest Crop Planting Area based on NDVI Sequential Characteristics[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(1): 144-148.
9 熊勤学, 黄敬峰. 利用 NDVI 指数时序特征监测秋收作物种植面积[J]. 农业工程学报, 2009, 25(1): 144-148.
10 FoersterS, KadenK, FoersterM, et al. Crop Type Mapping Using Spectral-temporal Profiles and Phenological Information[J]. Computers and Electronics in Agriculture, 2012, 89: 30-40.
11 WangLianxi, XuShengnan, LiQi, et al. Extraction of Winter Wheat Planted Area in Jiangsu Province Using Decision tree and Mixed-pixel Methods[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(5): 182-187.
11 王连喜, 徐胜男, 李琪,等. 基于决策树和混合像元分解的江苏省冬小麦种植面积提取[J]. 农业工程学报, 2016(5):182-187.
12 WangWenjing, ZhangXia, ZhaoYinde, et al. Cotton Extraction Method of Integrated Multi-features based on Multi-temporal Landsat 8 Images[J]. Journal of Remote Sensing, 2017, 21(1):115-124.
12 王文静, 张霞, 赵银娣, 等. 综合多特征的 Landsat 8 时序遥感图像棉花分类方法[J]. 遥感学报, 2017, 21(1): 115-124.
13 LiuJikai, ZhongShiquan, LiangWenhai,et al. Extraction on Crops Planting Structure based on Multi-temporal Landsat 8 OLI Images[J]. Remote Sensing Technology and Application, 2015, 30(4): 775-783.
13 刘吉凯, 钟仕全, 梁文海. 基于多时相Landsat 8 OLI影像的作物种植结构提取[J]. 遥感技术与应用, 2015, 30(4):775-783.
14 LiXiaohui, WangHong, LiXiaobing, et al. Study on Crops Remote Sensing Classification based on Multi-temporal Landsat 8 OLI Images[J]. Remote Sensing Technology and Application, 2019, 34(2):384-397.
14 李晓慧, 王宏, 李晓兵, 等.基于多时相Landsat 8 OLI 影像的农作物遥感分类研究[J]. 遥感技术与应用,2019,34(2):387-397.
15 ZhangRi, MaJianwen. A Feature Selection Algorithm for Hyperspectual Data with SVM-RFE[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7):834-837.
15 张睿, 马建文. 一种SVM-RFE高光谱数据特征选择算法[J]. 武汉大学学报(信息科学版), 2009, 34(7):834-837.
16 ShiFeifei, GaoXiaohong, YangLingyu, et al. Research on Jypical Crop Classification based on HJ-1A Hyperspectral Data in the Huangshui River Basin[J]. Remote Sensing Technology and Application, 2017, 32(2): 206-217.
16 史飞飞,高小红,杨灵王,等.基于HJ-1A高光谱遥感数据的湟水流域典型农作物分类研究[J]. 遥感技术与应用,2017,32(2):201-217.
17 PingYaopeng, ZangShuying. Crop Identification based on MODIS NDVI Time-series Data and Phenological Characteristics[J]. Journal of Natural Resources, 2016, 31(3): 503-513.
17 平跃鹏, 臧淑英. 基于 MODIS 时间序列及物候特征的农作物分类[J]. 自然资源学报, 2016, 31(3): 503-513.
18 KhatamiR, MountrakisG, StehmanS V. A Meta-analysis of Remote Sensing Research on Supervised Pixel-based Land-cover Image Classification Processes: General Guidelines for Practitioners and Future Research[J]. Remote Sensing of Environment, 2016, 177: 89-100.
19 KrizhevskyA, SutskeverI, HintonG E. Imagenet Classification with Deep Convolutional Neural Networks[C]⫽Advances in Neural Information Processing Systems. 2012: 1097-1105.
20 MakantasisK, KarantzalosK, DoulamisA, et al. Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks[C]⫽2015 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 2015: 4959-4962.
21 ZhangF, DuB, ZhangL. Saliency-Guided Unsupervised Feature Learning for Scene Classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(4):2175-2184.
22 ZhangKang, BaoqingHei, ZhouZhuang, et al. CNN with Coefficient of Variation-based Dimensionality Reduction for Hyperspectral Remote Sensing Images Classification[J]. Journal of Remote Sensing, 2018, 22(1):87-96.
22 张康,黑保琴, 周壮,等. 变异系数降维的CNN高光谱遥感图像分类[J]. 遥感学报, 2018, Journal of Remote Sensing, 2018, 22(1):87-96.
23 HuangYun, TangLinbo, LiZhen, et al. Research on Peanut Planting Area Classification Technology Using Remote Sensing Image based Deep Learning [J]. Journal of Signal Processing, 2019,35(4):617-622.
23 黄云,唐林波,李震,等.采用深度学习的遥感图像花生种植区域分类技术研究[J].信号处理,2019,35(4):617-622.
24 MaLi. Extracting Corn Planting Area by Multi-source Data with SVM Mixed-field Decomposed Method[D]. Xi'an: Xi'an University of Science and Technology, 2009.
24 马丽. 多源信息复合的 SVM 混合地块分解法提取玉米种植面积[D]. 西安: 西安科技大学, 2009.
25 LecunY, BottouL, BengioY, et al. Gradient-based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
26 HintonG E, SalakhutdinovR R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006, 313(5786): 504-507.
27 HuW, HuangY, WeiL, et al. Deep Convolutional Neural Networks for Hyperspectral Image Classification[J]. Journal of Sensors, 2015: 1-12.
28 YueJ, ZhaoW, MaoS, et al. Spectral–spatial Classification of Hyperspectral Images Using Deep Convolutional Neural Networks[J]. Remote Sensing Letters, 2015, 6(6): 468-477.
29 LiYandong, HaoZongbo, LeiHang. Survey of Convolutional Neural Network[J]. Journal of Computer Applications, 2016(9): 2508-2515.
29 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9): 2508-2515.
No Suggested Reading articles found!