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DropBand: A Simple and Effective Method for Promoting the Scene Classification Accuracy of Convolutional Neural Networks for VHR Remote Sensing Imagery

https://doi.org/10.1109/lgrs.2017.2785261
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.patcog.2016.07.001openalex-snapshot
Towards better exploiting convolutional neural networks for remote sensing scene classification
inferredcites →https://doi.org/10.1080/01431169608948714openalex-snapshot
The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/iccv.2009.5459469openalex-snapshot
What is the best multi-stage architecture for object recognition?
inferredcites →https://doi.org/10.1109/tgrs.2015.2488681openalex-snapshot
Scene Classification via a Gradient Boosting Random Convolutional Network Framework
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Bag-of-visual-words and spatial extensions for land-use classification
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DeepSat
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ImageNet classification with deep convolutional neural networks
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ADADELTA: An Adaptive Learning Rate Method
inferredcites →https://doi.org/10.48550/arxiv.1312.5402openalex-snapshot
Some Improvements on Deep Convolutional Neural Network Based Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1501.02876openalex-snapshot
Deep Image: Scaling up Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1509.03602openalex-snapshot
DeepSat - A Learning framework for Satellite Imagery
inferredcites →https://doi.org/10.5281/zenodo.1125018openalex-snapshot
Satellite Imagery Classification Based On Deep Convolution Network

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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DropFilterR: A Novel Regularization Method for Learning Convolutional Neural Networks
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Multi-Structure Joint Decision-Making Approach for Land Use Classification of High-Resolution Remote Sensing Images Based on CNNs
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An Augmentation Attention Mechanism for High-Spatial-Resolution Remote Sensing Image Scene Classification
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Robust Joint Representation of Intrinsic Mean and Kernel Function of Lie Group for Remote Sensing Scene Classification
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Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
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DCNNet: A Distributed Convolutional Neural Network for Remote Sensing Image Classification
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Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network
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