jyddjld1's starred repositories
ultralytics
Ultralytics YOLO11 🚀
Challenge_ZhongKeXingTu5_Sea_Ice
Second-place Solution for Sea Ice Segmentation in 2021 GaoFen Challenge
ViSual_IceD
Deep learning tool for automated sea ice detection in concurrent multispectral and synthetic aperture radar imagery.
sea-ice-segment
Sea ice segmentation using synthetic aperture radar and convolutional neural networks
sea_ice_remote_sensing
Deep Learning models for Sea Ice Concentration classification generated from the architectures of Neural Network, 1D-CNN and concatenation of the two.
Sea-Ice-Evaluation-Tool
The Sea Ice Evaluation Tool (SITool) is a performance metrics and diagnostics tool developed to evaluate the model skills in simulating the bi-polar sea ice concentration, extent, edge location, thickness, snow depth, and sea ice drift.
Fine-tuning-a-pre-trained-CNN-for-first-year-sea-ice-and-multi-year-sea-ice-cp-imagery-classificatio
Mapping first-year sea ice and multi-year sea ice in the oceans is significant for many applications. For example, ship navigation and weather forecast. Accurate and robust classification methods of multi-year ice and first-year ice are in demand [2]. Hybrid-polarity SAR architecture will be included in future SAR missions such as the Canadian RADARSAT Constellation Mission (RCM). These sensors will enable the use of compact polarimetry (CP) data in wide swath imagery [1]. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, I pursue this method for the classification of image patches from compact polarimety (CP) imagery into first-year ice and multi-year ice is applicable. In this course project, my work is kind of like the first practice of the CP imagery classification by fine-tuning a pre-trained convolutional neural network (CNN). Specifically, fine-tuning the last fully-connected layer of a pre-trained convolutional networks, I extract patches from simulated CP images as my dataset, the classification accuracy of the test set achieved 91.3% by fine-tuning a pre-trained CNN, compared to 49.4% classification accuracy by training from scratch.
ArcticSeaIce
Sea ice parameters
icenet-paper
Code associated with the paper 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'