Improving active learning methods using spatial information
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning. © 2011 IEEE.
Cross-reactivity between tumor MHC class I-restricted antigens and an enterococcal bacteriophage
International audience; Intestinal microbiota have been proposed to induce commensal-specific memory T cells that cross-react with tumor-associated antigens. We identified major histocompatibility complex (MHC) class I-binding epitopes in the tail length tape measure protein (TMP) of a prophage found in the genome of the bacteriophage Enterococcus hirae Mice bearing E. hirae harboring this prophage mounted a TMP-specific H-2Kb-restricted CD8+ T lymphocyte response upon immunotherapy with cyclophosphamide or anti-PD-1 antibodies. Administration of bacterial strains engineered to express the TMP epitope improved immunotherapy in mice. In renal and lung cancer patients, the presence of the ent…
Using active learning to adapt remote sensing image classifiers
The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and cluster…
Dataset shift adaptation with active queries
In remote sensing image classification, it is commonly assumed that the distribution of the classes is stable over the entire image. This way, training pixels labeled by photointerpretation are assumed to be representative of the whole image. However, differences in distribution of the classes throughout the image make this assumption weak and a model built on a single area may be suboptimal when applied to the rest of the image. In this paper, we investigate the use of active learning to correct the shifts that may appear when training and test data do not come from the same distribution. Experiments are carried out on a VHR remote sensing classification scenario showing that active learni…