Weakly Supervised Forest Fire Segmentation in UAV Imagery Based on Foreground-Aware Pooling and Context-Aware Loss
暂无分享,去创建一个
Ning Ye | Heng-fu Yin | Yupeng Wang | Liping Liu | Can Xu | Junling Wang
[1] Lili Feng,et al. The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China , 2023, Remote. Sens..
[2] X. Chen,et al. Economic Loss Assessment and Spatial–Temporal Distribution Characteristics of Forest Fires: Empirical Evidence from China , 2022, Forests.
[3] Dai Quoc Tran,et al. Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization , 2022, Int. J. Appl. Earth Obs. Geoinformation.
[4] Catarina Barata,et al. Weakly Supervised Fire and Smoke Segmentation in Forest Images with CAM and CRF , 2022, 2022 26th International Conference on Pattern Recognition (ICPR).
[5] Xing Wang,et al. Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards , 2022, Comput. Electron. Agric..
[6] W. Gonçalves,et al. Road extraction in remote sensing data: A survey , 2022, Int. J. Appl. Earth Obs. Geoinformation.
[7] Zhaoyou Lu,et al. Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks , 2022, Forests.
[8] Weiwei Cai,et al. A high-precision forest fire smoke detection approach based on ARGNet , 2022, Comput. Electron. Agric..
[9] W. Cui,et al. Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery , 2021, Remote. Sens..
[10] Jiali Shang,et al. Automated delineation of agricultural field boundaries from Sentinel-2 images using recurrent residual U-Net , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[11] Yanfeng Wang,et al. A High-precision Forest Fire Smoke Detection Approach Based on DRGNet to Remote Sensing Through Uavs , 2021 .
[12] Ming-Hsuan Yang,et al. Weakly Supervised Object Localization and Detection: A Survey , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Bumsub Ham,et al. Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Guosheng Lin,et al. Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[15] In-Jae Yu,et al. Puzzle-CAM: Improved Localization Via Matching Partial And Full Features , 2021, 2021 IEEE International Conference on Image Processing (ICIP).
[16] L. Jorge,et al. A Review on Deep Learning in UAV Remote Sensing , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[17] Myungjoo Kang,et al. Semantic Fire Segmentation Model Based on Convolutional Neural Network for Outdoor Image , 2021, Fire Technology.
[18] Abolfazl Razi,et al. Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset , 2020, Comput. Networks.
[19] Xilin Chen,et al. Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Roman Larionov,et al. Wildfire Segmentation on Satellite Images using Deep Learning , 2020, 2020 Moscow Workshop on Electronic and Networking Technologies (MWENT).
[21] Dimitrios Chrysostomou,et al. A Framework for Wildfire Inspection Using Deep Convolutional Neural Networks , 2020, 2020 IEEE/SICE International Symposium on System Integration (SII).
[22] Yi Wang,et al. Real-time forest smoke detection using hand-designed features and deep learning , 2019, Comput. Electron. Agric..
[23] S. Dimitropoulos. Fighting fire with science , 2019, Nature.
[24] Jesper E. van Engelen,et al. A survey on semi-supervised learning , 2019, Machine Learning.
[25] Neil Flood,et al. Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[26] Quoc V. Le,et al. Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Suha Kwak,et al. Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Mert R. Sabuncu,et al. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.
[29] Moulay A. Akhloufi,et al. Computer vision for wildfire research: An evolving image dataset for processing and analysis , 2017 .
[30] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.