Crop classification in cloudy and rainy areas based on the optical-synthetic aperture radar response mechanism

Abstract. In the agricultural field, optical remote sensing technology plays an important role in crop monitoring or production estimation. However, the widespread distribution of clouds and rain limits the application of optical remote sensing. Synthetic aperture radar (SAR) has been widely used for studies of oceans, atmosphere, land, and space exploration, as well as by the military due to its all-weather nature, penetration to surface and cloud layers, and diversity of information carriers. However, it is difficult to classify ground objects with high accuracy based on SAR data. Considering the features of these two datasets, we proposed a framework to improve crop classifications in cloudy and rainy areas based on the optical-SAR response mechanism. Specifically, this method is designed to train a parametric analytic model in the area using both kinds of datasets and applied in the area with only SAR data to obtain the optical time-series features. Then crops from the second area were classified by the long-short-term memory network. As an example, the parametric analytic model in Lixian County was studied and was applied to Xifeng County to classify the crops with the OA of 61%, which had proved the robustness of the method.

[1]  Patrick Hostert,et al.  Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Jiancheng Luo,et al.  Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception , 2020 .

[3]  Björn Waske,et al.  Random Feature Selection for Decision Tree Classification of Multi-temporal SAR Data , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[4]  Jiaxing Zhang,et al.  Attentional Neural Network: Feature Selection Using Cognitive Feedback , 2014, NIPS.

[5]  Shuang Xu,et al.  Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  J. Luo,et al.  Topographic constrained land cover classification in mountain areas using fully convolutional network , 2019, International Journal of Remote Sensing.

[7]  Guido Lemoine,et al.  Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Zhongliang Fu,et al.  RESEARCH ON IMAGE TRANSLATION BETWEEN SAR AND OPTICAL IMAGERY , 2012 .

[9]  Qingquan Li,et al.  Unsupervised Simplification of Image Hierarchies via Evolution Analysis in Scale-Sets Framework , 2017, IEEE Transactions on Image Processing.

[10]  Kristof Van Tricht,et al.  Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium , 2018, Remote. Sens..

[11]  M. Chakraborty,et al.  SAR signature investigation of rice crop using RADARSAT data , 2006 .

[12]  Wolfgang Wagner,et al.  Using ENVISAT ASAR Global Mode Data for Surface Soil Moisture Retrieval Over Oklahoma, USA , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[13]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[14]  Matthias Sperber,et al.  Self-Attentional Acoustic Models , 2018, INTERSPEECH.

[15]  Arnaud Mialon,et al.  MCM'10: An experiment for satellite multi-sensors crop monitoring from high to low resolution observations , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[16]  T. Blaschke,et al.  Object-based contextual image classification built on image segmentation , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[17]  Jiancheng Luo,et al.  Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data , 2019, GIScience & Remote Sensing.

[18]  Qingquan Li,et al.  Stepwise Evolution Analysis of the Region-Merging Segmentation for Scale Parameterization , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Jinwei Dong,et al.  High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data , 2019, Scientific Data.

[20]  Wang Yao,et al.  L 1/2 regularization , 2010 .

[21]  Xiaocheng Zhou,et al.  DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data , 2019, Remote. Sens..

[22]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[23]  Weimin Huang,et al.  A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Bahram Daneshfar,et al.  Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework , 2017 .

[25]  Giuseppe Satalino,et al.  Comparison of polarimetric SAR observables in terms of classification performance , 2008 .

[26]  Wei Wu,et al.  Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data , 2017, Remote. Sens..

[27]  Heather McNairn,et al.  Synthetic aperture radar and optical satellite data for estimating the biomass of corn , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Xiang Bai,et al.  Richer Convolutional Features for Edge Detection , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[30]  F. H. Newell,et al.  United States Geological Survey , 1900, Nature.

[31]  Li Yan.  RICE YIELD ESTIMATION IN REGIONAL SCALE BY USING RADARSAT SNB SAR IMAGES , 2003 .

[32]  Susan C. Steele-Dunne,et al.  Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands , 2019, Remote. Sens..

[33]  Krishna Prasad Vadrevu,et al.  Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Qingquan Li,et al.  A Bilevel Scale-Sets Model for Hierarchical Representation of Large Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Jiancheng Luo,et al.  Geo-Object-Based Soil Organic Matter Mapping Using Machine Learning Algorithms With Multi-Source Geo-Spatial Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[37]  C. G. J. Schotten,et al.  Assessment of the capabilities of multi-temporal ERS-1 SAR data to discriminate between agricultural crops , 1995 .

[38]  D. Amarsaikhan,et al.  The integrated use of optical and InSAR data for urban land‐cover mapping , 2007 .

[39]  Hao Liu,et al.  Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data , 2019, Sensors.

[40]  Jiancheng Luo,et al.  Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China , 2019, Geoderma.

[41]  Jiancheng Luo,et al.  Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set , 2019, Remote. Sens..

[42]  Avik Bhattacharya,et al.  Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data With Google Earth Engine , 2018, IEEE Geoscience and Remote Sensing Letters.

[43]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[44]  Wenjiang Huang,et al.  Applications of satellite 'hyper-sensing' in Chinese agriculture: Challenges and opportunities , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[45]  Phil Blunsom,et al.  Teaching Machines to Read and Comprehend , 2015, NIPS.

[46]  P. Nijkamp,et al.  Case Study of the Netherlands , 2001, Vessel-Source Pollution and Coastal State Jurisdiction.

[47]  Dipankar Mandal,et al.  Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox , 2019 .