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

HyperMINE – An Earth Observation Spatio-Temporal Data Mining System

The increasing number of satellite missions for Earth exploration provides huge amounts of data. These data are of wide diversity regarding the images characteristics, thus new techniques and tools need to be developed to accommodate the extraction of meaningful information. This paper presents An Earth Observation Spatio-Temporal Data Mining System (HyperMINE), which integrates fast and complex query methods in order to generate SITS (Satellite Image Time Series) regarded as a data hypercube. The multidimensional data, considering geographical space, time, band, and satellite/sensor are used as input for information mining algorithms. The system is built on a modular multilayer architecture that allows effective processing of various sources of data

Analysis of Bucharest's Land Cover Evolution Over a Period of 33 Years Using Multi-Sensor Data

Past and current EO (Earth Observation) satellite missions have gathered huge amount of data during the past decades. This offers the opportunity to retrieve significant information concerning the evolution of land cover for almost any point of interest (POI) on Earth's surface. This paper presents the evolution of land cover in the administrative area of Bucharest, Romania, over a time span of 33 years. In order to achieve this goal we use data acquired by multiple EO missions such as: Landsat 5 TM (Thematic Mapper), 7 ETM+ (Enhanced Thematic Mapper Plus), 8 OLI/TIRS (Operational Land Im-ager/Thermal InfraRed Sensor) and Sentinel-2 MSI (Multi-Spectral Instrument). We compute several spectral indexes in order to obtain information regarding the surface coverage evolution for categories such as vegetation, water bodies and build up

Improved Earth Observation Data Retrieval through Hashing Algorithms

Throughout the years, a wide range of satellite mission enabled the creation of a huge amount of Earth Observation (EO) data carrying complex information, whose exploitation is left behind due to the lack of handling capabilities. Computational resources are hardly keeping up with content analysis and information retrieval. In order to increase the search speed through data warehouses for knowledge discovery, new indexing methods are required to handle both the size and the informational complexity of EO data. Feature extraction algorithms are able to describe the image content, yet, they require a very complex database. In this paper, we propose a methodology that combines feature extraction, hashing methods and optimized indexing to convert the images characteristics into hash codes in an effort to speed up the search process. For our experiments, we run our procedure on a data-set composed of several Sentinel-2 acquisitions form across Europe and we assess the query times

Assessment of Burned Area Mapping Methods for Smoke Covered Sentinel-2 Data

Wildfires become more frequent in the context of global warming and severe drought in several parts of the globe. Earth observation data can be used to provide information in such cases, but sometimes, when using optical satellite imagery, the evaluation of the effects produced by ongoing large scale forest fires, can be impeded by smoke. It can reduce the accuracy of the information required by disaster management authorities when allocating resources. To improve both the usability of optical remote sensing data and the quality of the obtained information we compare multiple feature extraction, classification, and visual enhancement methods and algorithms for land cover mapping of smoke covered Sentinel-2 data. The demonstration is performed for the 2019 forest fires in Australia

An Active-Learning approach to the query by example retrieval in remote sensing images

In this paper, we propose an Active Learning approach to query by example retrieval, using a retraining procedure that improves the understanding of the machine with respect to the human perception. The proposed method is based on Support Vector Machine (SVM) classifiers and requires a small number of training samples. The classifier is retrained several times in order to determine the optimal separating hyper-plane between the class of the query and the rest of the analysed image. The closest feature points to the SVM-learned hyper-plane are the points being able to produce the most relevant modification of the position of this hyper-plane. These points, that are both negative and positive examples, are then used to retrain the SVM classifier. In addition, the proposed approach shows the importance of normalization in a classification problem with heterogeneous objects. Several experiments were conducted on GeoEye-1 multispectral images, whilst the retrieval was performed for different patch-level descriptors, which furthermore increases the complexity of the semantic content of the query object

Exploratory Search Methodology for Sentinel 2 Data: A Prospect of Both Visual and Latent Characteristics

Sentinel 2 (S2) satellite provides a systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and assist human understanding and semantic annotation in support of Earth surface mapping. This paper proposes an exploratory search methodology for S2 data underpinning both visual and latent characteristics by means of data visualization and content representation. For optimized results, the authors focus on a detailed assessment of top relevant state-of-the-art algorithms for features extraction and classification to determine which one could handle best the characteristics of S2 data