Multi-modal image matching Image registration Feature detection Deep learning Synthetic Aperture Radar (SAR) Optical imagery 1. Introduction Two of the most used modalities for space-borne remote sensing are Synthetic Aperture Radar (SAR) and optical imagery, since the information they provide about ob...
There are essential differences between optical and SAR remote sensing imaging in terms of mechanism, so the geometric and physical properties of optical and SAR images are extremely different, which makes it more difficult to match images. We designed and improved a deep learning network, firstly,...
A Framework for Deep Learning-based Sparse SAR-Optical Image Matching [Paper] Building the Docker Image docker build -t somatch:latest . Training the Matching Network docker run -it --rm --runtime=nvidia -v <your dataset root>:/src/data/ -v <your results directory>:/src/results -e CUD...
other state-of-the-art autofocus methods in sparsity-driven SAR imaging applications. 展开全部 机器翻译 AF-AMPNet: A Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing Shunjun WeiJiadian LiangMou Wang...Jinhe Ran IEEE Transactions on Geoscience and Remote Sensing...
One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image patches, collected from across ...
Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) an... Z Li,H Zhang,Y Huang - 《Remote Sensing》 被引量: 0发表: 2021年 Local convolutional features and metric learning for SAR image registra...
THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSIONM. Schmitt 1 , L. H. Hughes 1 , X. X. Zhu 1,21 Signal Processing in Earth Observation, Technical University of Munich (TUM), Munich, Germany - (m.schmitt,lloyd.hughes)@tum.de2 Remote Sensing Technology Institute (IMF)...
By using Ground Control Points (GCPs) derived from TerraSAR-X, the absolute geolocation accuracy of optical satellite images can be improved. For this purpose, the corresponding matching points in the optical images need to be localized. In this paper, a deep learning based approach is ...
Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and ...
A wide range of problems in applied physics and engineering involve learning physical displacement fields from data. In this paper we propose a deep neural network-based approach for learning displacement fields in an end-to-end manner, focusing on the specific case of particle image velocimetry (...