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

Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages appendix

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

The Digital Earth Observation Librarian: A Data Mining Approach for Large Satellite Images Archives

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR’s (German Aerospace Center’s) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany