Title: A Structured Approach to the Analysis of Remote Sensing Images,
Abstract: 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.