text: In this paper we present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global hand-crafted, local hand-crafted, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use Land Cover (LandUse) dataset and 19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted. Features extracted from SatResNet-50, a residual CNN suitable fine-tuned on the RS domain, shows much better performance than a residual CNN pre-trained on multimedia scene and object images. Features extracted from NetVLAD, a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine-grained textures and objects.
  @cite_38: This paper presents an overview of the challenges of producing a list of retrieval results ranked according to perceptual similarity. We explain some of the problems in using a metric to measure peceptual similarity, and consider the arguments for the desirability of metrics for retrieval. We discuss the use of broader definitions of betweenness to produce such a ranking of retrieval results. We propose some initial ideas of a notion of projective betweenness that makes explicit the intuition that two referents should be used when producing a similarity ranking, and indicate how it might be used in relevance feedback.
  @cite_22: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
summary: A basic retrieval scheme takes as input the visual descriptor corresponding to the query image perfomed by the user and it computes the similarity between such a descriptor and all the visual descriptors of the database of features. As a result of the search, a ranked list of images is returned to the user. The list is ordered by a degree of similarity, that can be calculated in several ways @cite_22 : Euclidean distance (that is the most used), Cosine similarity, Manhattan distance, @math -square distance, etc. @cite_38 .