Encoding Invariances in Remote Sensing Image Classification With SVM
This letter introduces a simple method for including invariances in support-vector-machine (SVM) remote sensing image classification. We design explicit invariant SVMs to deal with the particular characteristics of remote sensing images. The problem of including data invariances can be viewed as a problem of encoding prior knowledge, which translates into incorporating informative support vectors (SVs) that better describe the classification problem. The proposed method essentially generates new (synthetic) SVs from the obtained by training a standard SVM with the available labeled samples. Then, original and transformed SVs are used for training the virtual SVM introduced in this letter. We first incorporate invariances to rotations and reflections of image patches for improving contextual classification. Then, we include an invariance to object scale in patch-based classification. Finally, we focus on the challenging problem of including illumination invariances to deal with shadows in the images. Very good results are obtained when few labeled samples are available for classification. The obtained classifiers reveal enhanced sparsity and robustness. Interestingly, the methodology can be applied to any maximum-margin method, thus constituting a new research opportunity.