海雾检测参考文献

1、Cloud Image Retrieval for Sea Fog Recognition (CIR-SFR) Using Double Branch Residual Neural Network （JSTARS）

propose a cloud image retrieval method for sea fog recognition (CIR-SFR)

The feature extraction module adopts the double branch residual neural network (DBRNN) to comprehensively extract the global and local features of cloud images.

and accurate SFR results are obtained by counting the percentage of various cloud image types in the retrieval results.

提出了一种用于海雾识别 (CIR-SFR) 的云图像检索方法，采用双分支残差神经网络（DBRNN）综合提取云图的全局和局部特征。对于基于检索的SFR模块，根据特征空间中的距离从云图像数据集中检索相似的云图像，通过统计检索结果中各种云图像类型的百分比来获得准确的SFR结果。

提出了一种用于海雾识别 (CIR-SFR) 的云图像检索方法，采用双分支残差神经网络（DBRNN）综合提取云图的全局和局部特征，并将提取结果在云图像数据集中进行检索。

2、Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study （JSTARS）

The experimental results of this study provide data support and an effective reference for attacks on and the defense capabilities of various CNNs with regard to adversarial examples AEs in Synthetic aperture radar SAR image classification models.

本研究的实验结果为各种CNN对SAR图像分类模型中AE的能力提供了数据支持和有效参考。

4、LSCIDMR: Large-Scale Satellite Cloud Image Database for Meteorological Research

Thus, we propose a large-scale cloud image database for meteorological research (LSCIDMR). Several representative deep learning methods are evaluated on the proposed LSCIDMR, and the results can serve as useful baselines for future research.

提出了一个用于气象研究的大规模云图像数据库（LSCIDMR），评估了几种具有代表性的深度学习方法，结果可以作为未来研究的有用基线

Bai C, Zhang M, Zhang J, et al. LSCIDMR: Large-scale satellite cloud image database for meteorological research[J]. IEEE Transactions on Cybernetics, 2021, 52(11): 12538-12550.

5、Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images （JSTARS）

To effectively use such various modalities for clouds and weather systems identification through satellite image classification tasks, we propose a new satellite image classification framework: multimodal auxiliary network (MANET). MANET consists of three parts: image feature extraction module based on convolutional neural network, meteorological information feature extraction module based on perceptron, and layer-level multimodal fusion.

为了通过遥感图像分类任务有效地使用各种模式进行云和天气系统识别，我们提出了一种新的遥感图像分类框架：多模式辅助网络（MANET）。 MANET由三部分组成：基于卷积神经网络的图像特征提取模块、基于感知器的气象信息特征提取模块、层级多模态融合。

Bai C, Zhao D, Zhang M, et al. Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 7333-7345.

6、A Novel Ground-Based Cloud Image Segmentation Method by Using Deep Transfer Learning

This letter established a new Ground-Based Cloud Segmentation (GBCS) data set with 1742 accurately labeled images. Then to evaluate how well deep learning models perform in cloud segmentation, 12 state-of-the-art semantic segmentation networks are selected, among which DeepLabV3+ outperformed all others.

建立了一个新的地面云分割 (GBCS) 数据集，评估深度学习模型在云分割中的表现

Zhou Z, Zhang F, Xiao H, et al. A novel ground-based cloud image segmentation method by using deep transfer learning[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.

7、MMSTN: A Multi-Modal Spatial-Temporal Network for Tropical Cyclone Short-Term Prediction

We propose a novel TC trajectory and intensity short-term prediction method: Multi-Modal Spatial-temporal Networks (MMSTN). It not only predicts the TC's central pressure, winds, and the location of its center, but also forecasts the TC's varied possible tendencies. （JSTARS）

我们提出了一种新颖的 TC 轨迹和强度短期预测方法：多模态时空网络 (MMSTN)。 它不仅预测了TC的中心气压、风向和中心位置，还预测了TC的各种可能趋势

Huang C, Bai C, Chan S, et al. MMSTN: A Multi‐Modal Spatial‐Temporal Network for Tropical Cyclone Short‐Term Prediction[J]. Geophysical Research Letters, 2022, 49(4): e2021GL096898.

8、Patch Matching-Based Multitemporal Group Sparse Representation for the Missing Information Reconstruction of Remote-Sensing Images （JSTARS）

Poor weather conditions and/or sensor failure always lead to inevitable information loss for remote-sensing images acquired by passive sensor platforms. This common issue makes the interpretation (e.g., target recognition, classification, change detection) of remote-sensing data more difficult. Toward this end, this paper proposes to reconstruct the missing information of optical remote-sensing data by patch matching-based multitemporal group sparse representation (PM-MTGSR).

本文提出通过基于块匹配的多时相群稀疏表示（PM-MTGSR）来重建光学遥感数据的缺失信息。解决恶劣的天气条件和/或传感器故障导致被动传感器平台获取的遥感图像不可避免的信息丢失。

Li X, Shen H, Li H, et al. Patch matching-based multitemporal group sparse representation for the missing information reconstruction of remote-sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8): 3629-3641.