Automated delineation of agricultural field boundaries from Sentinel-2 images using recurrent residual U-Net
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Jiali Shang | Qiangzi Li | Huanxue Zhang | Bin Li | Yuji Wang | Mingxu Liu | Xiangliang Liu | Aiqi Song | J. Shang | Huanxue Zhang | Qiangzi Li | Mingxu Liu | Aiqi Song | Xianglian Liu | Bin Li | Yuji Wang
[1] John F. Canny,et al. A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] W. Cohen,et al. Lidar Remote Sensing for Ecosystem Studies , 2002 .
[3] Qingjie Liu,et al. Road Extraction by Deep Residual U-Net , 2017, IEEE Geoscience and Remote Sensing Letters.
[4] Patricia Gober,et al. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.
[5] Alfred Stein,et al. Recurrent Multiresolution Convolutional Networks for VHR Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[6] John Ray Bergado,et al. Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping , 2019, Remote sensing of environment.
[7] Mustafa Türker,et al. Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping , 2013 .
[8] Yun Shi,et al. 3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images , 2018, Remote. Sens..
[9] Jungho Im,et al. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification , 2010 .
[10] George Vosselman,et al. A deep learning approach to DTM extraction from imagery using rule-based training labels , 2018 .
[11] Ali Feizollah,et al. Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.
[12] Shiming Xiang,et al. Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[13] ByungSoo Ko,et al. Naive semi-supervised deep learning using pseudo-label , 2018, Peer-to-Peer Networking and Applications.
[14] Amit Verma,et al. An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis , 2019, IEEE Access.
[15] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[16] Adriaan Van Niekerk,et al. A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery , 2019, Comput. Electron. Agric..
[17] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[18] Marina Mueller,et al. Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery , 2004, Pattern Recognit..
[19] Mark Berman,et al. Segmenting multispectral Landsat TM images into field units , 2002, IEEE Trans. Geosci. Remote. Sens..
[20] Yong Jiang,et al. Fast mutual modulation fusion for multi-sensor images , 2015 .
[21] Michael Kampffmeyer,et al. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[22] Jordan Graesser,et al. Detection of cropland field parcels from Landsat imagery , 2017 .
[23] Bin Chen,et al. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.
[24] Lorenzo Bruzzone,et al. A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[25] H. Mcnairn,et al. Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier , 2018 .
[26] Pedro Antonio Gutiérrez,et al. Object-Based Image Classification of Summer Crops with Machine Learning Methods , 2014, Remote. Sens..
[27] Christopher O. Justice,et al. Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..
[28] J. Tollefson. Seven billion and counting , 2011, Nature.
[29] Peter Caccetta,et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[30] Uwe Stilla,et al. Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks , 2016, IEEE Geoscience and Remote Sensing Letters.
[31] Haipeng Wang,et al. Target Classification Using the Deep Convolutional Networks for SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[32] Lin Yan,et al. Automated crop field extraction from multi-temporal Web Enabled Landsat Data , 2014 .
[33] Martin Volk,et al. The comparison index: A tool for assessing the accuracy of image segmentation , 2007, Int. J. Appl. Earth Obs. Geoinformation.
[34] M. Perkuhn,et al. Automated Meningioma Segmentation in Multiparametric MRI , 2020, Clinical Neuroradiology.
[35] Vijayan K. Asari,et al. Improved inception-residual convolutional neural network for object recognition , 2017, Neural Computing and Applications.
[36] François Waldner,et al. Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network , 2019, ArXiv.
[37] Mariana Belgiu,et al. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .
[38] Curt H. Davis,et al. Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.
[39] Lei Guo,et al. When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[40] Steffen Fritz,et al. Estimating the global distribution of field size using crowdsourcing , 2018, Global change biology.
[41] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Consuelo Gonzalo-Martin,et al. A machine learning approach for agricultural parcel delineation through agglomerative segmentation , 2017 .
[44] Fan Xia,et al. Assessing object-based classification: advantages and limitations , 2009 .
[45] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[46] Bo Chen,et al. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty , 2015, Remote. Sens..
[47] Paola Passalacqua,et al. Learning a River Network Extractor Using an Adaptive Loss Function , 2018, IEEE Geoscience and Remote Sensing Letters.
[48] Felix Rembold,et al. Analysis of GAC NDVI Data for Cropland Identification and Yield Forecasting in Mediterranean African Countries , 2001 .
[49] Jianping Fan,et al. Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..
[50] Arno Schäpe,et al. Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .
[51] Md Zahangir Alom,et al. Recurrent residual U-Net for medical image segmentation , 2019, Journal of medical imaging.
[52] L. Joshua Leon,et al. Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..
[53] Guifeng Zhang,et al. An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation , 2010, IEEE Transactions on Image Processing.
[54] Zhang Miao,et al. Research on Crop Identification Using Multi-temporal NDVI HJ Images , 2015 .
[55] David Morin,et al. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series , 2015, Remote. Sens..
[56] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] R. Congalton,et al. Integrating cloud-based workflows in continental-scale cropland extent classification , 2018, Remote Sensing of Environment.
[58] Mutlu Ozdogan,et al. Large area cropland extent mapping with Landsat data and a generalized classifier , 2018, Remote Sensing of Environment.
[59] Huashan Liu,et al. A Novel Post-Processing Method Based on a Weighted Composite Filter for Enhancing Semantic Segmentation Results , 2020, Sensors.
[60] Qing Liu,et al. Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[61] Stéphane Dupuy,et al. A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) , 2017, Remote. Sens..
[62] R. Congalton,et al. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .
[63] Jitendra Malik,et al. Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[64] S. Weise,et al. Effectiveness of agro-ecological intensification practices in managing pests in smallholder banana systems in East and Central Africa , 2013 .
[65] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[66] Claudio Persello,et al. Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks , 2019, Remote. Sens..
[67] Gunilla Borgefors,et al. Integrated method for boundary delineation of agricultural fields in multispectral satellite images , 2000, IEEE Trans. Geosci. Remote. Sens..