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Journal of Applied Remote Sensing • new

Do deep convolutional neural networks really need to be deep when applied for remote scene classification?
Author(s): Chang Luo; Jie Wang; Gang Feng; Suhui Xu; Shiqiang Wang
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Paper Abstract

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for remote scene classification, there are not sufficient images to train a very deep CNN from scratch. From two viewpoints of generalization power, we propose two promising kinds of deep CNNs for remote scenes and try to find whether deep CNNs need to be deep for remote scene classification. First, we transfer successful pretrained deep CNNs to remote scenes based on the theory that depth of CNNs brings the generalization power by learning available hypothesis for finite data samples. Second, according to the opposite viewpoint that generalization power of deep CNNs comes from massive memorization and shallow CNNs with enough neural nodes have perfect finite sample expressivity, we design a lightweight deep CNN (LDCNN) for remote scene classification. With five well-known pretrained deep CNNs, experimental results on two independent remote-sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in an unsupervised setting. However, because of its shallow architecture, LDCNN cannot obtain satisfactory performance, regardless of whether in an unsupervised, semisupervised, or supervised setting. CNNs really need depth to obtain general features for remote scenes. This paper also provides baseline for applying deep CNNs to other remote sensing tasks.

Paper Details

Date Published: 18 September 2017
PDF: 24 pages
J. Appl. Rem. Sens. 11(4) 042613 doi: 10.1117/1.JRS.11.042613
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Chang Luo, Air Force Engineering Univ. (China)
Jie Wang, Air Force Engineering Univ. (China)
Gang Feng, Air Force Engineering Univ. (China)
Suhui Xu, Xi'an Research Institute of High Technology (China)
Shiqiang Wang, Air Force Engineering Univ. (China)

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