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20162021
most citedConvolution Neural Network Architecture Learning for Remote Sensing Scene Classification

14 citations · 19 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation

Yuanyi Zhong, Bodi Yuan, Hong Wu +3

We present a novel semi-supervised semantic segmentation method which jointly achieves two desiderata of segmentation model regularities: the label-space consistency property betwe…

cs.CV2020

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…

cs.CV202014 cited

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…

cs.CV2018

Understanding the Importance of Single Directions via Representative Substitution

Li Chen, Hailun Ding, Qi Li +3

Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior. The interpretation of individual units, which are neurons in MLPs or…

cs.CV2017

On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition

Jie Chen, Chao Yuan, Min Deng +3

Human vision possesses strong invariance in image recognition. The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hi…

cs.CV20175 cited

What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?

Haifeng Li, Jian Peng, Chao Tao +2

Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition. Compared with the natur…