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.

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.

In this paper, we present the visualization of image databases based on their primitive features. Our approach is to have a visual navigation tool for allowing the exploration and exploitation of large image archives. The tool is able to project the content of a given image database based on the primitive feature space and to provide interaction between the final user and the huge amount of data. Land Use/Land Cover area frame statistical Survey in-situ data are used as test dataset. Bag-of-Words and Weber Local Descriptors are used as primitive features.

Typical applications based on remote sensing imagery start with a visual inspection and selection of scenes and regions of interest to be purchased. In this letter, we propose a so-called Earth observation (EO) epitome as a novel value-added interactive product. Basically, it comprises several image tiles, primitive features, metadata entries, a quick look of the image tiles, and semantic annotations of the image content. It comes with a browser that enables the end user to perform a visual inspection of the image content and semantic annotation of the image objects based on machine learning methods. The epitome offers a more complete description of the content of an EO product. It is envisaged that the epitome will be integrated into the TerraSAR-X Payload Ground Segment and will be offered via user services.

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.

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.

The increasing number of Earth Observation image acquisitions provides huge volume of information, which requires new techniques, methods and tools to explore and exploit the abundant volume of information contained in the image archives. This paper presents an architecture concept for data mining systems using Earth Observation images. The architecture concept is defined as a modular system composed of five modules allowing functions such as primitive feature extraction, image content and context analyses, finding scenes of interest by content, enriched metadata, and semantics, the interpretation and understanding of the image content, semantic definition of the image content, and visualization of the image database via human machine interfaces. All these functionalities are integrated and supported by a database management system.

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.

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.

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.

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.

TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults) and Semantic Web technologies (stRDF and stSPARQL) implemented on top of a state of the art column store database system (MonetDB). We demonstrate a first prototype of the TELEIOS Virtual Earth Observatory (VEO) architecture, using a forest fire monitoring application as example.

Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. The first method uses a Gauss–Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined using both supervised and unsupervised classifications, and confusion matrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images.

This paper discusses methods and parameter settings that help to estimate texture in SAR images. In general, this is a difficult task for SAR images that are characterized by speckle noise and which span a wide range of pixel magnitudes. We applied Gauss Markov Random Field (GMRF) models and Enhanced Model Based Despeckling (EMBD) to 1 meter resolution amplitude images of the German TerraSAR-X mission. The results demonstrate that one can find appropriate parameter combinations that allow robust texture estimation even for different types of target areas.

Synthetic aperture radar (SAR) images are affected by multiplicative noise called speckle. This noise makes automatic image classification and image interpretation difficult. Thus, many methods have been developed to remove speckle from SAR images while preserving the useful information of the scene such as texture and geometry. In this letter, a comparison between three different despeckling methods based on a Bayesian approach and Gibbs random fields is made. The used methods are Gauss–Markov random field (GMRF) and autobinomial modeling, which operate in the image domain, and the GMRF approach, which operates in the wavelet domain. Our methods are evaluated with synthetic and real SAR data (TerraSAR-X images). The experimental results show that, with these three methods, the speckle is well removed while structures are preserved; quantitative measures show that the autobinomial method provides the best smoothness and sharpness criteria in real SAR data, while the wavelet-based method generates the smallest bias.

High resolution remote sensing SAR images — such as the image data acquired by the German TerraSAR-X mission — contain a variety of details that have to be extracted by automated processing in order to fully exploit and understand the image content. In particular, the interpretation of man-made structures that are typical of built-up or agricultural areas poses a number of challenges including parameterized image focusing during routine processing, careful despeckling, descriptor and feature extraction, and final classification including specific scattering and 3D effects. Therefore, we propose a set of general sequential as well as dedicated application-dependent processing steps that allow user-oriented classification of high resolution SAR images. We will also report on actual classification results and experiences.

While typical remote sensing imaging instruments produce more and more data, what we miss today are reliable tools for automated information extraction form these images. In the following, we propose a so-called Content Map, a novel Earth Observation value adding product. Basically, it comprises several class files and a viewer showing the different classes of land use and objects contained in the corresponding image data. In order to avoid processing delays, the class files have to be generated in an unsupervised mode as a real time product; thus, interactive user interactions have to be limited to training and testing intervals. As typical examples we use image data of the German TerraSAR-X mission that produces SAR image data in a variety of different modes.

Compared to conventional optical images, the classification of remote sensing SAR images represents a rather difficult task. As a rule, the various SAR imaging and product options, the high dynamic range of SAR images, and the presence of speckle noise prevent us from obtaining robust classification results. In the following, we try to circumvent these difficulties by proper pre-processing and despeckling of high resolution SAR images. Our approach aims at adapting the radiometry and the resolution of the scenes to the optimal target recognition capabilities of a classification algorithm. To this end, we have to apply systematic adaptations that provide optimized multilooking of our input data. Then these adapted data can be fed into a scene classification system.