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A structured approach to the analysis of remote sensing images.
- Source :
-
International Journal of Remote Sensing . Oct2019, Vol. 40 Issue 20, p7874-7897. 24p. 2 Diagrams, 5 Charts, 9 Graphs, 1 Map. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01431161
- Volume :
- 40
- Issue :
- 20
- Database :
- Academic Search Index
- Journal :
- International Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 136978559
- Full Text :
- https://doi.org/10.1080/01431161.2019.1607611