The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time. Index Terms-local binary pattern, content based image retrieval, remote sensing.
Recently, Tekeste et al. REF presented a comparative study of Local Binary Pattern (LBP) descriptor for remote sensing data retrieval.
Advanced Local Binary Patterns for Remote Sensing Image Retrieval
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Computer Science