Content-Based Remote Sensing Image Retrieval

Dear Colleagues,

During the last decade we have witnessed an exponential growth in the data collection interest all around every aspect of our lives. The technological requirements demanded to effectively analyze such rapid accumulation of vast amounts of data has yield to the development of novel distributed systems to support the management of such big data infrastructures. This has naturally lead to a bloom of data-analysis methods developments, particularly in the machine learning field.

The Remote Sensing community has been also actively involved in this new paradigm. High performance sensors has led to an ever-increasing of public and private geospatial repositories. Not only the massiveness of remote sensing data volumes presents a challenge for the Remote Sensing community, the complexity of said data has also flourished with the diversity of sensors resolutions (satellites, airbornes, drones), spectral capacities (LIDAR, SAR, polSAR, multi-, hyper- and ultra-spectral), image acquisition procedures (images, tiles, video sequences) and a large body of applications of interest (Earth Observation, planetology, astronomy, precision agriculture, crisis management, etc.).

The Image Analysis and Image Retrieval communities have taken great advantage of this new scenario and have benefited from both, the availability of huge amounts of data and the development of new techniques, e.g., deep learning models. Still, remote sensing data is a particularly challenging domain for the design and development of effective general-purpose retrieval systems:

* Data representations capable of handling multi-modality (particularly across different spatial and spectral resolutions).

* Effective user-oriented interfaces for querying and feedback management.

* Models to exploit heterogeneous and application-dependent user communities prior information.

* Standards for the deployment of big unstructured remote sensing data warehouses and scalable infrastructures.

* Scarcity of public benchmarks with reliable ground-truth and annotated images.

* Generative models to synthetize spatially and spectrally realistic remote sensing images.

* Models for real time interactive analysis and characterization of multi-modal remote sensing data.

* Exploitation of complementary data from the Internet of Things, i.e., smartphone sensors, portable measurement devices, wearables, etc.

The purpose of this Special Issue is to highlight the urgent necessity of delivering novel remote sensing image retrieval systems that could effectively extract valuable information from a variety of remote sensing data sources and could match the diverse needs of the increasing variety of scientific and industrial applications demanded by practitioners.

Topics of interest:

* Content- and context-based indexing, search and retrieval of RS data

* Search and browsing on RS big data repositories to face the Peta/Zettabyte scale

* Advanced descriptors and similarity metrics dedicated to multi-modal RS data

* Usage of prior knowledge and semantic information for retrieval in RS

* Matching learning for image retrieval in remote sensing

* Query models, paradigms, and languages dedicated to RS

* Multimodal/multi-observations (sensors, dates, resolutions) analysis of RS data

* HCI issues in RS retrieval and browsing

* Evaluation of RS retrieval systems

* Dissemination of publicly available RS groundtruth and annotated images

* RS image generative processes

* High performance indexing algorithms for RS data

* Real-time information retrieval techniques and applications

* Summarization and visualization of very large satellite image datasets

* Applications of image retrieval in remote sensing

* Models and applications of RD data and IoT data hybrid approaches

Dr. Miguel A. Veganzones

 

Guest Editors

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