Search Results for author:
Found 10 papers, 5 papers with code
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field.
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
In this paper, we propose a Transformer-based decoder and construct a UNet-like Transformer (UNetFormer) for real-time urban scene segmentation.
Ranked #1 on
Scene Segmentation
on UAVid
Multiattention network for semantic segmentation of fine-resolution remote sensing images
A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention.
Ranked #7 on
Semantic Segmentation
on ISPRS Vaihingen
Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images
However, MSR images suffer from two critical issues: 1) increased scale variation of geo-objects and 2) loss of detailed information at coarse spatial resolutions.
Inference for extreme earthquake magnitudes accounting for a time-varying measurement process
We consider how improvements to data quality over time can be incorporated when selecting a modelling threshold and in the subsequent inference of an extreme value analysis.
Methodology Applications 62G32
Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning.
MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images.
A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet
Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels.
Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection
The prediction of this Phase is the set of changed and unchanged superpixels.
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.
