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Found 6 papers, 0 papers with code
Image Fusion in Remote Sensing: An Overview and Meta Analysis
Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images.
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images
All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view).
A Review of Mobile Mapping Systems: From Sensors to Applications
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades.
Fine-tuning deep learning models for stereo matching using results from semi-global matching
Knowing that classical stereo matching methods such as Census-based semi-global-matching (SGM) are widely adopted to process different types of stereo data, we therefore, propose a finetuning method that takes advantage of disparity maps derived from SGM on target stereo data.
Spatiotemporal Fusion in Remote Sensing
Remote sensing images and techniques are powerful tools to investigate earth surface.
3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets
The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multitemporal data set.
