Spatial and Structured SVM for Multilabel Image Classification

Satoru Koda, Abdallah Zeggada, Farid Melgani, Ryuei Nishii

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


We describe a novel multilabel classification approach based on a support vector machine (SVM) for the extremely high-resolution remote sensing images. Its underlying ideas consist to: 1) exploit inter-label relationships by means of a structured SVM and 2) incorporate spatial contextual information by adding to the cost function a term that encourages spatial smoothness into the structural SVM optimization process. The resulting formulation appears as an extension of the traditional SVM learning, in which our proposed model integrates the output structure and spatial information simultaneously during the training. Numerical experiments conducted on two different UAV- and airborne-acquired sets of images show the interesting properties of the proposed model, in particular, in terms of classification accuracy.

Original languageEnglish
Article number8363005
Pages (from-to)5948-5960
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number10
Publication statusPublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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