Scene classification of high-resolution remote sensing image combining data augmentation and transfer learning

doi:10.13474/j.cnki.11-2246.2020.0041

Abstract

Abstract: Deep learning has achieved remarkable results in the field of computer vision, such as image classification, face recognition, image retrieval and so on. For remote sensing, obtaining a labeled dataset for training DCNN is often a major challenge. In this paper, the use of DCNN for scene classification in high-resolution remote sensing imagery is investigated. In order to overcome the lack of a large number of labeled remote sensing image datasets, two technologieswere combined with DCNN:data augmentation and transfer learning. On the UC Merced Land Use dataset, the performances of 5 networks including VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 were verified, which achieved classification accuracy of 98.10%, 96.19%, 99.05%, 97.62%, and 99.52%, respectively.

Key words: high-resolution remote sensing imagery, scene classification, convolutional neural network, data augmentation, transfer learning

CLC Number:

P237

QIAO Tingting, LI Luqun. Scene classification of high-resolution remote sensing image combining data augmentation and transfer learning[J]. Bulletin of Surveying and Mapping, 2020, 0(2): 37-42.

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