Daniela Espinoza-Molina

Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata

Advances in the image retrieval (IR) field have contributed to the elaboration of tools for interactive exploration and extraction of the images from huge archives associating the content of the images with semantic meaning. This paper presents an Earth-observation (EO) IR system based on enriched metadata, semantic annotations, and image content called EO retrieval. EO retrieval generates an EO-data model by using automatic feature extraction, processing the EO product metadata, and defining semantics, which later is fully exploited for supporting complex queries. In order to demonstrate the functionality of the system, we have created a semantic catalog of TerraSAR-X as application scenario. The database is composed of 39 high-resolution TerraSAR-X scenes comprising about 50 000 image patches (160 × 160 pixels) with their feature descriptors, 100 of metadata entries for each scene, and about 330 semantic annotations. Many query examples combining semantics, metadata, and image content for full exploitation of the image database are presented.

Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, satellite image archives have been constantly increasing in size in the last few years (now reaching petabyte sizes), and have become a valuable source of information for many science and application domains (environment, oceanography, geology, archaeology, security, etc.). TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on scientific databases, linked geospatial data, ontologies and techniques for discovering knowledge from satellite images and auxiliary data sets. In this paper we outline the vision of TELEIOS (now in its second year), and give details of its original contributions on knowledge discovery from satellite images and auxiliary datasets, ontologies, and linked geospatial data.

Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs).

Query by example in Earth-Observation image archive using data compression-based approach

This paper presents an implementation of query by example in Earth Observation image archive using data compression-based approach. Data compression approach allows to exploit the compression properties of the objects and to estimate the shared information between them, this concept is extended to image retrieval for finding similar objects in the image archive. Our implementation is based on LZW algorithm for compressing the image content and extracting features of the images. The fast compression distance (FCD) is defined as a similarity metric in order to retrieve the most similar images. This tool is satisfactory implemented and tested using optical and SAR images.

Multilayer Architecture for Heterogeneous Geospatial Data Analytics: Querying and Understanding EO Archives

The constantly growing process of the Earth Observation (EO) data and their heterogeneity require new systems and tools for effectively querying and understanding the available data archives. In this paper, we present a tool for heterogeneous geospatial data analytics. The system implements different web technologies in a multilayer server–client architecture, allowing the user to visually analyze satellite images, maps, and in-situ information. Specifically, the information managed is composed of EO multispectral and synthetic aperture radar products along with the multitemporal in-situ LUCAS surveys. The integration of these data provides a very useful information during the EO scene interpretation process. The system also offers interactive tools for the detection of optimal datasets for EO multitemporal image change detection, providing at the same time ground-truth points for both human and machine analyses. Furthermore, we show by means of visual analytic representations a way to analyze and understand the content and distribution of the EO databases.

LUCAS Visual Browser: A tool for land cover visual analytics

In this paper we present the LUCAS Visual Browser system, a tool for land cover visual analytics. The system implements different web technologies in a multilayer server-client architecture in order to allow the user to visually analyse land cover heterogeneous information. The information manage is composed of EO multispectral and SAR products along with the multitemporal in situ LUCAS surveys. The fusion of these data provides a very useful information during the EO scene interpretation process. Furthermore, the system offers interactive tools for the detection of optimal datasets for EO multi-temporal image change detection, providing at the same time ground truth points for both, human and machine analysis.

Assessment of Earth Observation data content based on data compression - application to settlements understanding

Urban areas around the world are rapidly changing in an unregulated manner and remote sensing is the most effective option for their monitoring and planning. Good modeling of urban areas means reliable translation of the scene semantics into an algorithmic language. The compression based image retrieval techniques are data driven. The intention of employing compression based image retrieval techniques is to exploit the compression properties of the objects and estimate the shared information between them. Fast compression distance (FCD) is the similarity metric used in a compression based image retrieval technique that can be applied on large datasets. FCD between any two objects can be computed using the sizes of their dictionaries (sequence of recurring patterns) extracted through compression with LZW algorithm and the intersection of their dictionaries. In this paper, it is proposed to assess high resolution Earth Observation data content based on data compression for understanding urban settlements.

Multisensor Earth Observation Image Classification Based on a Multimodal Latent Dirichlet Allocation Model

Many previous researches have already shown the advantages of multisensor land-cover classification. Here, we propose an innovative land-cover classification approach based on learning a joint latent model of synthetic aperture radar (SAR) and multispectral satellite images using multimodal latent Dirichlet allocation (mmLDA), a probabilistic generative model. It has already been successfully applied to various other problems dealing with multimodal data. For our experiments, we chose overlapping SAR and multispectral images of two regions of interest. The images were tiled into patches and their local primitive features were extracted. Then each image patch is represented by SAR and multispectral bag-of-words (BoW) models. The BoW values are both fed to the mmLDA, resulting in a joint latent data model. A qualitative and quantitative validation of the topics based on ground-truth data demonstrate that the land-cover categories of the regions are correctly classified, outperforming the topics obtained using individual single modality data.

Land-cover change detection using local feature descriptors extracted from spectral indices

An effective monitoring and analysis of ecosystems requires developing new tools and knowledge. In this paper, we propose an approach for detecting land-cover changes using satellite Image Time Series. This approach represents each image by spectral indices and then extracts local features of these representations. Next, a clustering technique (e.g., k-means) is applied to the extracted features, where the resulting clusters are assumed to refer to land-cover classes. The land-cover change is then obtained by counting the number of times an assigned class to each point changes along the time series. For our experiments, we use a collection of Landsat-5 images captured every second month from October 2009 to August 2010 over the protected area of the Doñana National Park in southwestern Spain, which is the largest sanctuary for migratory birds in western Europe. Results demonstrate that the proposed approach can detect the occurring changes in the main land-cover categories along the assessed time series.

On the statistical similarity of synthetic aperture radar images from COSMO-SKYMED and TerraSAR-X

The latest generation of synthetic aperture radar (SAR) instruments operating in X-band, that is, COSMO-SkyMed (CSK) and TerraSAR-X (TSX), are capable of providing images from coarse resolution to very high resolution. A lot of research effort has been invested in the study and understanding of images obtained from these satellites. However, there is still a huge scope of statistical understanding and comparison of data from both satellites. In this study, we demonstrate some striking similarities between medium resolution data obtained from CSK and TSX Stripmap mode images. Landcover unsupervised clustering using k-means is discussed to further justify our findings. Clustering is carried out using a feature descriptor based on log-cumulants of Gabor coefficients, which was recently proposed by us in earlier studies.