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

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)

[thumbnail of Cheng-etal-IEEE-TOGARS-2015-Effective-and-efficient-midlevel-visual-elements-oriented-land-use]
Text. Filename: Cheng_etal_IEEE_TOGARS_2015_Effective_and_efficient_midlevel_visual_elements_oriented_land_use.pdf
Accepted Author Manuscript

Download (3MB)| Preview


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.


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