Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.
Code (0)
등록된 구현이 없습니다.
Tasks
Image RetrievalRetrievalSimilar Papers 제목 키워드 기반
Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images
Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be…
Deep HashingImage RetrievalRetrievalSemantic RetrievalPIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation Learning
Remote sensing image-text retrieval constitutes a foundational aspect of remote sensing interpretation tasks, facilitating the alignment of vision and language representations. This paper introduces a prior instruction r…
Image-text RetrievalRepresentation LearningRetrievalScene Recognition+1Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only …
Image RetrievalRetrievalUnsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network
The advent of deep perceptual networks brought about a paradigm shift in machine vision and image perception. Image apprehension lately carried out by hand-crafted features in the latent space have been replaced by deep …
DecoderImage RetrievalRetrievalRegion Convolutional Features for Multi-Label Remote Sensing Image Retrieval
Conventional remote sensing image retrieval (RSIR) systems usually perform single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image. This assu…
Image RetrievalRetrievalSegmentation