Paper
18 October 2016 The true false ground truths: What interest?
K. Chehdi, C. Cariou
Author Affiliations +
Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100040M (2016) https://doi.org/10.1117/12.2241096
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom
Abstract
The existence of a few unreliable ground truth (GT) data sets which are often used as reference by the remote sensing community for the assessment and comparison of classification results is really problematic and poses a number of questions. Two of these ground truth data sets can be cited: "Pavia University" and "Indian Pine". A rigorous analysis of spectral signatures of pixels in these images shows that some classes which are considered as homogeneous from the ground truth are clearly not, since the pixels which belong to the same classes have different spectral signatures, and probably do not belong to the same category.

The persistence in using data sets from a biased ground truth does not allow objective comparisons between classification methods and does not contribute to providing explanation of physical phenomena that images are supposed to reflect.

In this communication, we present a fine and complete analysis of the spectral signatures of pixels within each class for the two ground truth data sets mentioned above. The metrics used show some incoherence and inaccuracy of these data which wrongly serve as references in several classification comparative studies.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. Chehdi and C. Cariou "The true false ground truths: What interest?", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040M (18 October 2016); https://doi.org/10.1117/12.2241096
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Hyperspectral imaging

Remote sensing

Visualization

Image classification

Image processing

Data communications

Scientific classification systems

Back to Top