Quality assessment of classification and cluster maps without ground truth knowledge

This work focuses on two challenging types of problems related to quality assessment and comparison of thematic maps generated from remote sensing (RS) images when little or no ground truth knowledge is available. These problems occur when: 1) competing thematic maps, generated from the same input RS image, assumed to be available, must be compared, but no ground truth knowledge is found to assess the accuracy of the mapping problem at hand, and 2) the generalization capability of competing classifiers must be estimated and compared when the small/unrepresentative ground truth problem affects the RS inductive learning application at hand. Specifically focused on badly posed image classification tasks, this paper presents an original data-driven (i.e., unsupervised) thematic map quality assessment (DAMA) strategy complementary (not alternative) in nature to traditional supervised map accuracy assessment techniques, driven by the expensive and error-prone digitization of ground truth kno...