Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks
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Huihui Song | Guojie Wang | Xikun Wei | Haolu Li | Zhen Dong | Mengjuan Wu | Solomon Obiri Yeboah Amankwah | Huihui Song | Guojie Wang | Haolun Li | Mengjuan Wu | Xikun Wei | S. Amankwah | Zheng Dong
[1] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Congcong Li,et al. An all-season sample database for improving land-cover mapping of Africa with two classification schemes , 2016 .
[3] Shen Shuang-he,et al. Crop classification by remote sensing based on spectral analysis , 2012 .
[4] Yi Li,et al. Impact of climate change on cotton growth and yields in Xinjiang, China , 2020 .
[5] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Nataliia Kussul,et al. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.
[7] M. Weiss,et al. Remote sensing for agricultural applications: A meta-review , 2020 .
[8] Min Feng,et al. A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm , 2016, Int. J. Digit. Earth.
[9] Jianxi Huang,et al. Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches , 2020, Remote. Sens..
[10] B. Brisco,et al. Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .
[11] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[12] 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.
[13] Sushil Pradhan,et al. Crop area estimation using GIS, remote sensing and area frame sampling , 2001 .
[14] Lan-hai Li,et al. Simulating impacts of climate change on cotton yield and water requirement using RZWQM2 , 2019, Agricultural Water Management.
[15] Hala M. Ebeid,et al. Machine Learning for Enhancement Land Cover and Crop Types Classification , 2018, Machine Learning Paradigms.
[16] Hector Erives,et al. Automated registration of hyperspectral images for precision agriculture , 2005 .
[17] Chunsheng Liu,et al. Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images , 2020, Remote. Sens..
[18] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[19] Liang Tong,et al. Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier , 2020, Remote. Sens..
[20] Alberto Tellaeche,et al. A computer vision approach for weeds identification through Support Vector Machines , 2011, Appl. Soft Comput..
[21] Pierre Alliez,et al. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[22] Yongchun Xie,et al. Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO , 2017, IEEE Transactions on Image Processing.
[23] Wolfgang Förstner. Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images , 2000 .
[24] Luis Álvarez,et al. A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Xia Zhang,et al. Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[26] Giles M. Foody,et al. Crop classification by support vector machine with intelligently selected training data for an operational application , 2008 .
[27] Gunter Menz,et al. Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.
[28] Anton van den Hengel,et al. Wider or Deeper: Revisiting the ResNet Model for Visual Recognition , 2016, Pattern Recognit..
[29] Horst Bischof,et al. Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..
[30] Marcel Salathé,et al. Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..
[31] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[32] Clement Atzberger,et al. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..
[33] David M. W. Powers,et al. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.
[34] Huihui Song,et al. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks , 2020, Remote. Sens..
[35] S. K. Srivastav,et al. Satellite Remote Sensing: Sensors, Applications and Techniques , 2017, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences.
[36] Jianxi Huang,et al. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information , 2015 .