Abstract
In the existing studies on remote sensing image scene classification, the supervised learning methods which are fine-tuned from pre-trained model require a large amount of labeled training data and parameters, while unsupervised learning methods do not make full use of label information, and the classification performance could be improved. In this paper, we introduced semi-supervised learning into generative adversarial network (GAN), so the discriminator learned more discriminative features from labeled data and unlabeled data. Moreover, the mixup data augmentation method was introduced into our classification model to augment the data and stabilized the training process. We carried out extensive experiments for both UC-Merced and NWPU-RESISC45 datasets with a 5-fold cross-validation protocol using a linear SVM as classifier. We trained the proposed method on UC-Merced dataset and achieve an average overall accuracy of 94.05% under 80% training ratio. When trained on NWPU-RESISC45 dataset, the proposed method reached an average overall accuracy of 83.12% and 92.78% under the training ratios of 20% and 80% respectively, which achieves the state-of-the-art deep learning methods without pre-training.
Highlights
The currently available instruments for earth observation generate more and more different types of airborne or satellite images with different resolutions [1]
We propose a classification model based on generative adversarial network (GAN) with semi-supervised learning, aiming to improve the feature extraction ability of the discriminator and the classification performance
We introduce the mixup data augmentation method into GAN to relieve the problem of small dataset, and stabilize the training process
Summary
The currently available instruments (e.g. multispectral, hyperspectral, synthetic aperture radar, etc.) for earth observation generate more and more different types of airborne or satellite images with different resolutions [1]. P. Yan et al.: Semi-Supervised Representation Learning for Remote Sensing Image Classification Based on GANs latter learns to extract features from data automatically. In the existing studies on remote sensing image scene classification, the state-of-the-art results are achieved through end to end supervised feature representation learning finetuned from pre-trained model which require a large amount of labeled training data and parameters. The existing GAN based remote sensing image classification methods are still unsupervised for feature extraction, because these methods do not use the labels of the training data and the generated images of the generator which are used to train GAN are unlabeled as well. To make full use of label information of the real data and unlabeled generated data, we proposed a semi-supervised learning model based on GAN for remote sensing image scene classification.
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