A Structured Approach to the Analysis of Remote Sensing Images
  The number of studies for the analysis of remote sensing images has been
growing exponentially in the last decades. Many studies, however, only report
results---in the form of certain performance metrics---by a few selected
algorithms on a training and testing sample. While this often yields valuable
insights, it tells little about some important aspects. For example, one might
be interested in understanding the nature of a study by the interaction of
algorithm, features, and the sample as these collectively contribute to the
outcome; among these three, which would be a more productive direction in
improving a study; how to assess the sample quality or the value of a set of
features etc. With a focus on land-use classification, we advocate the use of a
structured analysis. The output of a study is viewed as the result of the
interplay among three input dimensions: feature, sample, and algorithm.
Similarly, another dimension, the error, can be decomposed into error along
each input dimension. Such a structural decomposition of the inputs or error
could help better understand the nature of the problem and potentially suggest
directions for improvement. We use the analysis of a remote sensing image at a
study site in Guangzhou, China, to demonstrate how such a structured analysis
could be carried out and what insights it generates. The structured analysis
could be applied to a new study, or as a diagnosis to an existing one. We
expect this will inform practice in the analysis of remote sensing images, and
help advance the state-of-the-art of land-use classification.
