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Journal of Applied Remote Sensing • new

Multiscale deep features learning for land-use scene recognition
Author(s): Baohua Yuan; Shijin Li; Ning Li
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Paper Abstract

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Paper Details

Date Published: 9 February 2018
PDF: 12 pages
J. Appl. Rem. Sens. 12(1) 015010 doi: 10.1117/1.JRS.12.015010
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Baohua Yuan, Hohai Univ. (China)
Nanjing Univ. of Science and Technology (China)
Shijin Li, Hohai Univ. (China)
Ning Li, Hohai Univ. (China)

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