Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images

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Cheng, Gong and Han, Junwei and Guo, Lei and Liu, Zhenbao and Bu, Shuhui and Ren, Jinchang (2015) Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53 (8). pp. 4238-4249. ISSN 0196-2892 (https://doi.org/10.1109/TGRS.2015.2393857)

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Abstract

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.

ORCID iDs

Cheng, Gong, Han, Junwei, Guo, Lei, Liu, Zhenbao, Bu, Shuhui and Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194;
CORE (COnnecting REpositories)