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

Gong Cheng, Junwei Han, Lei Guo, Zhenbao Liu, Shuhui Bu, Jinchang Ren

Research output: Contribution to journalArticle

204 Citations (Scopus)
341 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)4238-4249
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number8
Early online date20 Feb 2015
DOIs
Publication statusPublished - Aug 2015

Keywords

  • land-use classification
  • object detectors
  • auto-encoder
  • neural network
  • very high resolution (VHR) remote sensing images

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