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Found 5 papers, 1 papers with code
Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade.
Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images
Firstly, a multi-branch segmentation network is built to learn an expert for each source RSI.
Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps
However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases. To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road. Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results.
Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction
This strategy estimates the depth map at coarsest level, while the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual.
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification
First, a deep densely connected convolutional network is considered for hyperspectral image classification.
