LodgeNet: Improved rice lodging recognition using semantic segmentation of UAV high-resolution remote sensing images
出版年份 2022 全文链接
标题
LodgeNet: Improved rice lodging recognition using semantic segmentation of UAV high-resolution remote sensing images
作者
关键词
Deep learning, U-Net, Small sample data set, End-to-end neural network
出版物
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 196, Issue -, Pages 106873
出版商
Elsevier BV
发表日期
2022-03-17
DOI
10.1016/j.compag.2022.106873
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- An improved U-Net method for the semantic segmentation of remote sensing images
- (2021) Zhongbin Su et al. APPLIED INTELLIGENCE
- Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)
- (2020) Hengbiao Zheng et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
- (2020) Foivos I. Diakogiannis et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Building Extraction Based on U-Net with an Attention Block and Multiple Losses
- (2020) Mingqiang Guo et al. Remote Sensing
- Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network
- (2020) Dongyan Zhang et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Identifying sunflower lodging based on image fusion and deep semantic segmentation with UAV remote sensing imaging
- (2020) Zhishuang Song et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Scene Segmentation With Dual Relation-Aware Attention Network
- (2020) Jun Fu et al. IEEE Transactions on Neural Networks and Learning Systems
- Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach
- (2019) Norman Wilke et al. Remote Sensing
- Attention gated networks: Learning to leverage salient regions in medical images
- (2019) Jo Schlemper et al. MEDICAL IMAGE ANALYSIS
- Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network
- (2019) Yaning Yi et al. Remote Sensing
- Estimates of rice lodging using indices derived from UAV visible and thermal infrared images
- (2018) Tao Liu et al. AGRICULTURAL AND FOREST METEOROLOGY
- Road Extraction by Deep Residual U-Net
- (2018) Zhengxin Zhang et al. IEEE Geoscience and Remote Sensing Letters
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- (2018) Liang-Chieh Chen et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Semantic image segmentation using fully convolutional neural networks with multi-scale images and multi-scale dilated convolutions
- (2018) Duc My Vo et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation
- (2018) Liang Han et al. Remote Sensing
- U-Net: deep learning for cell counting, detection, and morphometry
- (2018) Thorsten Falk et al. NATURE METHODS
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery
- (2017) Ming-Der Yang et al. Remote Sensing
- Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images
- (2017) Tianxing Chu et al. Remote Sensing
- Gibberellin Deficiency Confers Both Lodging and Drought Tolerance in Small Cereals
- (2016) Sonia Plaza-Wüthrich et al. Frontiers in Plant Science
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