High Spatial Resolution Remote Sensing Scene Classification Method using Transfer Learning and Deep Convolutional Neural Network

Deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high spatial resolution remote sensing (HSRRS) scenes, due to its powerful feature extraction capabilities. It is well-known that huge high quality labeled datasets are required for achieving the better classification performances and preventing over-fitting, during the training DeCNN model process. However, the lack of high quality datasets often limits the applications of DeCNN. In order to solve this problem, in this paper, we propose a HSRRS image scene classification method using transfer learning and DeCNN (TL-DeCNN) model in few shot HSRRS scene samples. Specifically, three typical DeCNNs of VGG19, ResNet50 and InceptionV3, trained on the ImageNet2015, the weights of their convolutional layer for that of the TL-DeCNN are transferred, respectively. Then, TL-DeCNN just needs to fine-tune its classification module on the few shot HSRRS scene samples in a few epochs. Experimental results indicate that our proposed TL-DeCNN method provides absolute dominance results without over-fitting, when compared with the VGG19, ResNet50 and InceptionV3, directly trained on the few shot samples.

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Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.2988477 ◽

2020 ◽

Vol 13 ◽

pp. 1986-1995 ◽

Cited By ~ 2

Author(s):

Wenmei Li ◽

Ziteng Wang ◽

Yu Wang ◽

Jiaqi Wu ◽

Juan Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Deep Convolutional Neural Network

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Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2018.04.050 ◽

2018 ◽

Vol 214 ◽

pp. 73-86 ◽

Cited By ~ 128

Author(s):

Bo Huang ◽

Bei Zhao ◽

Yimeng Song

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Use ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Urban Land Use ◽

Deep Convolutional Neural Network ◽

Remote Sensing Imagery ◽

Multispectral Remote Sensing

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Deep convolutional neural network structure design for remote sensing image scene classification based on transfer learning

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/569/1/012046 ◽

2020 ◽

Vol 569 ◽

pp. 012046

Author(s):

Xiaoxia Zhang ◽

Yong Guo ◽

Xia Zhang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Network Structure ◽

Remote Sensing Image ◽

Structure Design ◽

Deep Convolutional Neural Network ◽

Scene Classification ◽

Neural Network Structure

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Semantic Segmentation of Remote Sensing Images Using Transfer Learning and Deep Convolutional Neural Network With Dense Connection

IEEE Access ◽

10.1109/access.2020.3003914 ◽

2020 ◽

Vol 8 ◽

pp. 116744-116755 ◽

Cited By ~ 1

Author(s):

Binge Cui ◽

Xin Chen ◽

Yan Lu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Semantic Segmentation ◽

Deep Convolutional Neural Network ◽

Remote Sensing Images

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Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis

Remote Sensing ◽

10.3390/rs12010086 ◽

2019 ◽

Vol 12 (1) ◽

pp. 86 ◽

Cited By ~ 14

Author(s):

Rafael Pires de Lima ◽

Kurt Marfurt

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Network Models ◽

Remote Sensing Image ◽

Natural Images ◽

Natural Image ◽

Scene Classification ◽

Neural Network Models

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

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