期刊
REMOTE SENSING
卷 9, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/rs9090917
关键词
convolutional neural network; spatial information; multiple scales; feature representation
类别
资金
- CRSRI Open Research Program [SN CKWV2017539/KY]
- National Natural Science Foundation of China [41671408, 41501439]
- Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing [16R02]
Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based algorithm which incorporates appearance and spatial information jointly at multiple scales for land-use scene classification to tackle these problems. Specifically, we introduce a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. The resulting multiscale descriptors are used to generate visual words by a general mapping strategy and produce multiscale correlograms of visual words. Then, an adaptive vector quantization of multiscale correlograms, termed multiscale correlatons, are applied to encode the spatial arrangement of visual words at different scales. Experiments with two publicly available land-use scene datasets demonstrate that our MDDC model is discriminative for efficient representation of land-use scene images, and achieves competitive classification results with state-of-the-art methods.
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