In our Caps-SSENet, a capsule-neural-mixing size mapping module is designed to transform the extracted image features into capsules and complete the estimation of ship sizes using informative feature correlations from dynamic routing.
Jun 6, 2023
In our Caps-SSENet, a capsule-neural- mixing size mapping module is designed to transform the extracted image features into capsules and complete the estimation.
Sep 2, 2024 · Experimental results based on measured SAR data show that the proposed method reduces the estimation error of ship sizes in SAR images in ...
Caps-SSENet: An Improved Estimation Method for SAR Ship Size · Chen, Jiannan · Liu, Yu · Wang, Xueqian · Zhang, Yiming · Jiang, Zhizhuo · Li, Gang · Zheng, ...
在这封信中，我们提出了一种基于胶囊网络的SAR SSE 改进方法，称为Caps-SSE 网络（SSENet）。在我们的Caps-SSENet 中，胶囊神经混合尺寸映射模块旨在将提取的图像特征转换 ...
The experiments and analyses show that the proposed method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images achieves an improved ...
A deep learning model to extract the ship size from Sentinel-1 synthetic aperture radar (SAR) images, named SSENet, which shows robustness on different ...
We propose a model to straight extract ship size from Synthetic Aperture Radar (SAR) images. The model is named as SSENet. The SSENet uses a ...
Caps-SSENet: An Improved Estimation Method for SAR Ship Size · IEEE Geoscience and Remote Sensing LettersPub Date: 2023-06-06. Ship maneuvering model ...
In this letter, we propose an improved method for SAR SSE based on the capsule network named Caps-SSE network (SSENet). In our Caps-SSENet, a capsule-neural- ...