Semisupervised manifold alignment of multimodal remote sensing images

Devis Tuia, Michele Volpi, Maxime Trolliet, Gustau Camps-Valls

Research output: Contribution to journalArticleAcademicpeer-review

102 Citations (Scopus)


We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor, and multiangular images is available. In these situations, images should ideally be spatially coregistered, corrected, and compensated for differences in the image domains. Such procedures require massive interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps, and foldings of the (typically nonlinear) manifolds where images lie. The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains. A MATLAB implementation of the proposed method is provided at code/ssma.htm.

Original languageEnglish
Article number6822608
Pages (from-to)7708-7720
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number12
Publication statusPublished - Dec 2014
Externally publishedYes


  • Classification
  • domain adaptation
  • feature extraction
  • graph-based methods
  • multiangular
  • multisource
  • multitemporal
  • very high resolution (VHR)


Dive into the research topics of 'Semisupervised manifold alignment of multimodal remote sensing images'. Together they form a unique fingerprint.

Cite this