This paper describes an image validation tool for the gen-eration of good quality Earth Observation (EO) benchmark datasets. We already developed an active learning based se-mantic annotation tool which allows user fast annotate images with few samples, the tool reached about 90% accuracy. A subsequent data cleaning tool then helps correct noisy data, thus increasing the number of correctly labeled images to be qualified as benchmark data. However, this work has an an-noying bottleneck, the manual correction via visual checks still costs a considerable amount of energy. Therefore, this paper aims to introduce an image validation tool and propose new metrics to distinguish different ambiguous cases within a dataset, based on pattern analysis of the data. The interactive visualization then enables users to visualize a dataset and explore unknown patterns. The benefits are two-fold: firstly, experiments show the proposed metrics greatly help decrease the manual labor whilst keeping the essential data, thus enhancing the de-gree of automation in the process of generating good quality benchmark datasets. Secondly, our approach provides possi-bilities to interactively visualize and explore very large-scale datasets in real time, thus providing help for further data mining.

In this paper, we describe an innovative content annotation method for high-resolution Synthetic Aperture Radar (SAR) images generating routinely user-defined semantic labels for sequences of small contiguous image patches, while the full surface areas of our images cover hundreds of km in width and length. Based on this method, we are able to generate a sea-ice dataset that is used in projects to validate the developed machine learning methods.

The increased number of free and open satellite images has led to new applications of these data. Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by satellite imagers, in particular, the identification and quantification of temporal changes. In this paper, we will present guidelines and practical examples of how to obtain reliable image patch classification results based on data mining techniques for detecting possible changes that can appear within a data set. Here, we will focus on a scenario, namely forest monitoring using Earth observation Synthetic Aperture Radar data acquired by Sentinel-1, and multispectral data acquired by Sentinel-2.

Advanced interpretation of satellite images calls for automated content analysis as well as interactive content search. A typical example of such systems is EOLib, an ESA funded project that already demonstrated the application potential of TerraSAR-X data within a satellite payload ground segment. In this paper, we analyze the validation results of image content classification using a large set of selected TerraSAR-X images. The classification was done with a cascaded learning method. The main advantage of this method is a coarse-to-fine approach for semantically annotating pixel patches with decreasing size. Once a reliable label is found for a given pixel patch, no further subdivision into still smaller patch sizes is necessary. This leads to a considerable reduction of the computational effort during classification of large-size satellite images.

In this letter, a velocity estimation method for moving ships in synthetic aperture radar (SAR) images is proposed based on a subaperture decomposition technique. In contrast to traditional methods, our method needs only a few SAR imaging parameters besides the SAR image itself. The behavior of moving ships in subaperture images is theoretically analyzed, and the ship motion parameters in azimuth direction are accurately estimated. The proposed approach was tested on real SAR stripmap images acquired by TanDEM-X, a twin satellite SAR constellation. The estimated azimuth velocities perfectly fit the data recorded by the international automatic identification system.

We propose a multilevel semantics discovery approach for bridging the semantic gap when mining high-resolution polarimetric synthetic aperture radar (PolSAR) remote sensing images. First, an Entropy/Anisotropy/Alpha-Wishart classifier is employed to discover low-level semantics as classes representing the physical scattering properties of targets (e.g., low-entropy/surface scattering/high anisotropy). Then, the images are tiled into patches and each patch is modeled as a bag-of-words, a histogram of the class labels. Next, latent Dirichlet allocation is applied to discover their higher level semantics as a set of topics. Our results demonstrate that topic semantics are close to human semantics used for basic land-cover types (e.g., grassland). Therefore, using the topic description (bag-of-topics) of PolSAR images leads to a narrower semantic gap in image mining. In addition, a visual exploration of the topic descriptions helps to find semantic relationships, which can be used for defining new semantic categories (e.g., mixed land-cover types) and designing rule-based categorization schemes.

In this paper, we consider the problem of satellite image classification, in which feature extraction is a critical step. One of the most prevalent methods is the Bag-of-Words (BoW) feature representation, which attains state-of-the-art performance in many applications. It has five steps: feature detection, local feature extraction, dictionary learning, feature coding, and feature pooling. In this paper, we focus on the second and third step. We propose a simple yet efficient feature extraction method within the BoW framework. It has two main advantages. Firstly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from small windows as low level features. Secondly, instead of using a time-consuming clustering algorithm for dictionary learning, a random dictionary is built and applied to feature space quantization. An extensive experimental evaluation has been performed and compared with other feature extraction methods. It is demonstrated that our feature extraction method is quite competitive for optical and SAR satellite image classification.

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.

In this letter, we carry out a comparative study of statistical models for multilook synthetic aperture radar amplitude images. Ten state-of-the-art statistical models are selected for comparison. To achieve a fair evaluation, we estimate all model parameters using the method of log-cumulants and apply the method to an image pyramid with varying pixel spacing (and resolution). The pyramid is created by different image product generation options. In addition to pixel spacing and resolution, we also consider the homogeneity of a scene for performance evaluation and we apply three performance measures. Through this study, it was found out that some models perform well for all resolutions, while the performance of other models depends heavily on the image content.

Recent development in the design of modern satellite ground segments include systems and tools for automated content analysis allowing users to conduct systematic semantic searches within satellite image data archives. The need for such tools becomes more and more pressing as future space-borne imaging sensors will deliver enormous quantities of data that cannot be studied manually. For instance, typical examples from a European perspective are described in [1] and [2]. Within this framework, the European Space Agency (ESA) has started to fund the Earth Observation Librarian (EOLib) project to set up the next generation of image information mining systems [3]. Here we report on the preparation of scenarios that are needed for training and to verify and optimize the performance of such systems.

The TerraSAR-X satellite has been fully operational since nearly 6 years and has delivered a very large quantity of high resolution SAR images. Among these images one can find a number of repeated acquisitions of selected target areas taken with nearly identical imaging parameters. These image time series data often cover the full seasonal cycle of a target area and lend themselves well to small-scale change detection; however, in the case of urban areas, we have to be aware of the quantitative impact of rain, frost and wind on high resolution SAR images. In particular, images of Western European cities are characterized by construction work concentrating on single buildings within a fully built-up city and by public green space changes. A quantitative analysis of urban time series data has to discriminate between definite changes of the urban landscape and the transient impacts of rain, frost and wind. The critical issue is how to identify and characterize these transient phenomena.

The recognition and analysis of moving targets in SAR images is a well-known topic in remote sensing. The public availability of high resolution SAR satellite images opened new perspectives for space-based small object recognition: traffic monitoring from space has matured into an established technique where the motion characteristics of ships and cars can be extracted routinely. A particular case of traffic monitoring is the search for moving trains in SAR images; here one can locate moving trains, determine their velocity, and identify the type and number of wagons. In the following, we concentrate on features to be extracted from TerraSAR-X images containing commuter trains in the area of Munich, Germany.

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.

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.

This paper discusses the basic paradigm of how image information mining methods work in the field of remote sensing. To this end, we compare our approaches to the approaches being used in the world of multimedia; then we discuss the annotation specifics of remote sensing data and describe the different types of remote sensing data that we are faced with today. We conclude with a description of algorithmic alternatives and compare several competing systems that are currently in use. Finally, we provide an outlook of what we expect in the near future.

This paper presents two approaches for the analysis of multi-temporal and multisensory data analysis. The first approach focuses on a novel similarity measure, named Mixed Information, derived from information measures and assessment of its performance for multitemporal analysis in change detection. The second approach proposes the use of Kullback-Leibler divergence between marginal distributions, efficiently approximated by cumulant based expansion series. Comparison between mixed information and Kullback-Leibler divergence based change detection is performed to confirm the usefulness of joint metric for analyzing multitemproal and multisensory data. Experimental results obtained confirm justification of the mixed information in multitemporal analysis.

This paper presents a non-parametric modeling scheme for high resolution SAR data, based on Short Time Fourier Transform which is able to integrate the radiometrical and morphological properties of the data, for object recognition, scene and target indexing, addressing the problem of large data base queries and information retrieval.. The method is assessed by using a Bayesian Support Vector Machine image search engine based on a hierarchical learning model. The method allowed for the recognition of over 30 different classes, both homogeneous and heterogeneous urban objects with high levels of details. Qualitative and quantitative measures for evaluation are presented and discussed.

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.

Nowadays huge volumes of Earth Observation data are available and increasing every day. Users are faced with the problem of having to extract and interpret information in large volumes of data, with the additional problem of images being annotated only by simple descriptors. Therefore, in this paper we present the Knowledge-centered Earth Observation system which offers interactive probabilistic information mining, among other functionalities, and two Support Vector Machine based Feature Extraction services integrated in the system.