Abstract
In the remote sensing field, ground-truth design for collecting training samples represents a tricky and critical problem since it has a direct impact on most of the subsequent image processing and analysis steps. In this paper, we propose a novel framework for assisting a human user in designing ground-truth by photointerpretation for optical remote sensing image classification. The proposed approach is (almost) completely automatic and comprehensive since it aims at assisting the human user from the first to the last step of the process. It is based on unsupervised methods of segmentation and clustering, in order to investigate both the spatial and the spectral information in the process of ground-truth design. The resulting ground-truth is classifier-free and can be further improved by making it classifier-driven through an active learning process. To validate the proposed framework, an experimental study was conducted on very high spatial resolution and hyperspectral images acquired by the IKONOS and the Reflective Optics System Imaging Spectrometer sensors, respectively. The obtained results show the usefulness and effectiveness of the proposed approach.