Remote Sensing Image Representation based on Hierarchical Histogram Propagation

International audienceMany methods have been recently proposed to deal with the large amount of data provided by high- resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relation- ships among regions in a hierarchy to iteratively prop- agate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low- level extraction approaches

Improving Texture Description in Remote Sensing Image Multi-Scale Classification Tasks By Using Visual Words

International audienceAlthough texture features are important for region- based classification of remote sensing images, the liter- ature shows that texture descriptors usually have poor performance when compared and combined with color descriptors. In this paper, we propose a bag-of-visual- words (BOW) "propagation" approach to extract tex- ture features from a hierarchy of regions. This strategy improves efficacy of feature as it encodes texture infor- mation independently of the region shape. Experiments show that the proposed approach improves the classi- fication results when compared with global descriptors using the bounding box padding strategy

Remote Sensing Image Representation based on Hierarchical Histogram Propagation

International audienceMany methods have been recently proposed to deal with the large amount of data provided by high- resolution remote sensing technologies. Several of these methods rely on the use of image segmentation algorithms for delineating target objects. However, a common issue in geographic object-based applications is the definition of the appropriate data representation scale, a problem that can be addressed by exploiting multiscale segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting histogram-based features from a hierarchy of regions for multiscale classification. The strategy, called H-Propagation, exploits the existing relation- ships among regions in a hierarchy to iteratively prop- agate features along multiple scales. The proposed method speeds up the feature extraction process and yields good results when compared with global low- level extraction approaches