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

High-throughput analysis and advanced search for visually-observed phenotypes

Title from PDF of title page (University of Missouri--Columbia, viewed on May 13, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Chi-Ren ShyuIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"The trend in many scientific disciplines today, especially in biology and genetics, is towards larger scale experiments in which a tremendous amount of data is generated. As imaging of data becomes increasingly more popular in experiments related to phenotypes, the ability to perform high-throughput big data analyses and to efficiently locate specific information within these data based on increasingly complicated and varying search criteria is of great importance to researchers. This research develops several methods for high-throughput phenotype analysis. This notably includes a registration algorithm called variable object pattern matching for mapping multiple indistinct and dynamic objects across images and detecting the presence of missing, extra, and merging objects. Research accomplishments resulted in a number of unique advanced search mechanisms including a retrieval engine that integrates multiple phenotype text sources and domain ontologies and a search method that retrieves objects based on temporal semantics and behavior. These search mechanisms represent the first of their kind in the phenotype community. While this computational framework is developed primarily for the plant community, it has potential applications in other domains including the medical field.Includes bibliographical references