# A Structured Approach to the Analysis of Remote Sensing Images

## TL;DR

This paper proposes a structured analysis framework for remote sensing image studies, focusing on the interplay of features, samples, and algorithms to better understand and improve land-use classification results.

## Contribution

It introduces a novel structured decomposition approach to analyze the effects of features, samples, and algorithms on classification performance in remote sensing.

## Key findings

- Decomposes error into contributions from features, samples, and algorithms.
- Provides insights into which factors most influence classification accuracy.
- Demonstrates the approach with a case study in Guangzhou, China.

## 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.