A structured approach to the analysis of remote sensing images. Issue 20 (18th October 2019)

• Record Type: Journal Article
• Authors: Yan, Donghui; Li, Congcong; Cong, Na; Yu, Le; Gong, Peng
• Is Part Of: International journal of remote sensing. Volume 40:Issue 20(2019)
• Page Start: 7874
• Page End: 7897
• Publication Date: 2019-10-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1607611
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The number of studies for the analysis of remote sensing images has been growing exponentially in the last two 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 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. 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.