In the recent years, kernel methods have revealed very powerful tools
in many application domains in general and in remote sensing image
classification in particular. The special characteristics of remote
sensing images (high dimension, few labeled samples and different
noise sources) are efficiently dealt with kernel machines. In this
paper, we propose the use of structured output learning to improve
remote sensing image classification based on kernels. Structured
output learning is concerned with the design of machine learning
algorithms that not only implement input-output mapping, but also
take into account the relations between output labels, thus generalizing
unstructured kernel methods. We analyze the framework and introduce
it to the remote sensing community. Output similarity is here encoded
into SVM classifiers by modifying the model loss function and the
kernel function either independently or jointly. Experiments on a
very high resolution (VHR) image classification problem shows promising
results and opens a wide field of research with structured output
kernel methods.