Search Results for author:
Found 8 papers, 4 papers with code
Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory
This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.
Learning by Active Forgetting for Neural Networks
This paper presents a learning model by active forgetting mechanism with artificial neural networks.
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.
Ranked #4 on
Traffic Prediction
on SZ-Taxi
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition
Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images.
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data.
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding?
