On the De-Ramping of SLC-IW Tops SAR Data and Ocean Circulation Parameters Estimation

The spectral characteristics of single-look complex - inter-ferometric wide (SLC-IW) swath, terrain observation by progressive scan (TOPS), are significantly different from those of strip-map (SM). Due to the burst mode and series of sub-swaths, the target area is scanned for a short period of time. Therefore, swath width comes at the expense of azimuth resolution. To eliminate quadratic phase drift and achieve SLC baseband, significant processing is required. De-ramping is a necessary step to compute ocean circulation parameters. In this work, we extract ocean parameters from the complex echo signal based on data driven Doppler centroid (fDC) regardless of the OCN product information and geophysical fDC image. The radial surface velocity (RSV) is retrieved from Doppler history, and the significant wave height (SWH) is estimated with an empirical relationship of RSV. The results of ocean circulation parameters are promising when compared with benchmark and in-situ data. This work demonstrates the efficacy and necessity of de-ramping the TOPS data for subsequent use in a variety of ocean remote sensing applications

Data Vaults: a Database Welcome to Scientific File Repositories

Efficient management and exploration of high-volume scientific file repositories have become pivotal for advancement in science. We propose to demonstrate the Data Vault, an extension of the database system architecture that transparently opens scientific file repositories for efficient in-database processing and exploration. The Data Vault facilitates science data analysis using high-level declarative languages, such as the traditional SQL and the novel array-oriented SciQL. Data of interest are loaded from the attached repository in a just-in-time manner without need for up-front data ingestion. The demo is built around concrete implementations of the Data Vault for two scientific use cases: seismic time series and Earth observation images. The seismic Data Vault uses the queries submitted by the audience to illustrate the internals of Data Vault functioning by revealing the mechanisms of dynamic query plan generation and on-demand external data ingestion. The image Data Vault shows an application view from the perspective of data mining researchers

Improving knowledge discovery from synthetic aperture radar images using the linked open data cloud and Sextant

In the last few years, thanks to projects like TELEIOS, the linked open data cloud has been rapidly populated with geospatial data some of it describing Earth Observation products (e.g., CORINE Land Cover, Urban Atlas). The abundance of this data can prove very useful to the new missions (e.g., Sentinels) as a means to increase the usability of the millions of images and EO products that are expected to be produced by these missions. In this paper, we explain the relevant opportunities by demonstrating how the process of knowledge discovery from TerraSAR-X images can be improved using linked open data and Sextant, a tool for browsing and exploration of linked geospatial data, as well as the creation of thematic maps

Building Virtual Earth Observatories using Ontologies and Linked Geospatial Data

TELEIOS is a 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 database technologies (array databases, SciQL, data vaults), Semantic Web technologies (stRDF and stSPARQL) and linked geospatial data. In this technical communication we outline the TELEIOS advancements to the state of the art and give an overview of its technical contributions up to today

Mining large satellite image repositories using semi-supervised methods

The increasing number and resolution of earth observation (EO) imaging sensors has had a significant impact on both the acquired image data volume and the information content in images. There is consequently a strong need for highly efficient search tools for EO image databases and for search methods to automatically identify and recognize structures within EO images. In this paper, we present a concept for an earth observation image data mining system mixing an auto-annotation component with a category search engine which combines a generic image class search and an object detection feature

Indexation of large satellite image repositories using small training sets

Content Based Image Retrieval (CBIR) and automatic image annotation systems have been designed to tackle the problem of image retrieval in large image databases

Cascaded active learning for object retrieval using multiscale coarse to fine analysis

In this paper, we describe an active learning scheme which performs coarse to fine testing using a multiscale patch-based representation of images to retrieve objects in large satellite image repositories. The proposed hierarchical top-down approach reduces step by step the size of the analysis window, eliminating each time large parts of the images considered as non-relevant

Active learning using the data distribution for interactive image classification and retrieval

In the context of image search and classification, we describe an active learning strategy that relies on the intrinsic data distribution modeled as a mixture of Gaussians to speed up the learning of the target class using an interactive relevance feedback process