684 citations · 1.2k across the 12 of their papers we have counts for
4 papers · 1 filter
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
Haifeng Li, Zhenqi Cui, Zhiqing Zhu +4
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few d…
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Jie Chen, Ziyang Yuan, Jian Peng +5
Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change i…
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
Jie Chen, Haozhe Huang, Jian Peng +5
Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutio…
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
Enqiang Guo, Xinsha Fu, Jiawei Zhu +4
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…