Gottfried Schwarz

Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies

Currently, the amount of collected Earth Observation (EO) data is increasing considerably with a rate of several Terabytes of data per day. As a consequence of this increasing data volume, new concepts for exploration and information retrieval are urgently needed. To this end, we propose to explore satellite image data via an image information mining (IIM) approach in which the main steps are feature extraction, classification, semantic annotation, and interactive query processing. This leads to a new process chain and a robust taxonomy for the retrieved categories capitalizing on human interaction and judgment. We concentrated on land cover categories that can be retrieved from high-resolution synthetic aperture radar (SAR) images of the spaceborne TerraSAR-X instrument, where we annotated different urban areas all over the world and defined a taxonomy element for each prevailing surface cover category. The annotation resulted from a test dataset comprising more than 100 scenes covering diverse areas of Africa, Asia, Europe, the Middle East, and North and South America. The scenes were grouped into several collections with similar source areas and each collection was processed separately in order to discern regional characteristics. In the first processing step, each scene was tiled into patches. Then the features were extracted from each patch by a Gabor filter bank and a support vector machine with relevance feedback classifying the feature sets into user-oriented land cover categories. Finally, the categories were semantically annotated using Google Earth for ground truthing. The annotation followed a multilevel approach that allowed the fusion of information being visible on different resolution levels. The novelty of this paper lies in the fact that a semantic annotation was performed with a large number of high-resolution radar images that allowed the definition of more than 850 surface cover categories. This opens the way toward an automated identification and classification of urban areas, infrastructure (e.g., airports), geographic objects (e.g., mountains), industrial installations, military compounds, vegetation, and agriculture. Applications that may result from this work can be a semantic catalog of urban images to be used in crisis situations or after a disaster. In addition, the proposed taxonomies can become a basis for building a semantic catalog of satellite images. Finally, we defined four powerful types of high-level queries. Querying on such high levels provides new opportunities for users to search an image database for specific parameters or semantic relationships.

A Comparative Study of Statistical Models for Multilook SAR Images

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.

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.

A Benchmark Evaluation of Similarity Measures for Multitemporal SAR Image Change Detection

Synthetic aperture radar (SAR) image change detection is playing an important role in various Earth Observation (EO) applications. There exists a large number of different methods that have been proposed to address this issue. However, due to the fact that several kinds of changes with diverse characteristics can arise in SAR images, there is no consensus on their performances because most methods have been evaluated using different data sets, probably facing several kinds of changes, but without an in-depth analysis of the characteristics of SAR image changes. Therefore, two important problems arise. The first is what kind of change each approach can detect. The second is how much they can detect a kind of change. Although the importance to model any kind of changes has been realized, there is no principled methodology to carry out the analysis due to the difficulty in modeling various kinds of changes. In this paper, we propose a benchmark methodology to reach this goal by simulating selected kinds of changes in addition to using real data with changes. Six kinds of SAR changes for eight typical image categories are simulated, i.e., reflectivity changes, first-order, second-order, and higher order statistical changes, linear and nonlinear changes. Based on this methodology for change simulation, a comprehensive evaluation of information similarity measures is carried out. An explicit conclusion we have drawn from the evaluation is that the various methods behave very differently for all kinds of changes. We hope that this study will promote the advancement of this topic.

Remote Sensing Image Classification: No Features, No Clustering

In this paper, we consider the problem of remote sensing image classification, in which feature extraction and feature coding are critical steps. Various feature extraction methods aim at an abstract and discriminative image representation. Most of them are either theoretically too complex or practically infeasible to compute for large datasets. Motivated by this observation, we propose a simple yet efficient feature extraction method within the bag-of-words (BoW) framework. It has two main innovations. First and most interestingly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from a local window as low level features. Second, in contrast to many unsupervised feature learning methods, a random dictionary is applied to feature space quantization. The advantage of a random dictionary is that it does not need the time-consuming process of dictionary learning yet without a significant loss of classification accuracy. These two novel improvements over state-of-the-art methods significantly reduce the computational time and enable it scalable to a large data volume. 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 and can achieve rather promising performance figures for both optical and SAR satellite images.

Quality evaluation of compressed optical and SAR images: JPEG vs. wavelets

Presents a comparison of compression algorithms using the discrete cosine transform-DCT (JPEG) and discrete wavelet transform-DWT applied to remotely sensed images. The statistical behaviors of the DCT and DWT are addressed and the implications for the performance of the image compression algorithms are compared for optical and SAR images. These SAR images were despeckled during compression. Qualitative and quantitative results are presented.

Building virtual earth observatories using ontologies and linked geospatial data

Advances in remote sensing technologies have enabled public and commercial organizations to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation (EO) data has been constantly increasing in volume in the last few years, and is currently reaching petabytes in many satellite archives. For example, the multi-mission data archive of the TELEIOS partner German Aerospace Center (DLR) is expected to reach 2PB next year, while ESA estimates that it will be archiving 20PB of data before the year 2020. As the volume of data in satellite archives has been increasing, so have the scientific and commercial applications of EO data. Nevertheless, it is estimated that up to 95% of the data present in existing archives has never been accessed, so the potential for increasing exploitation is very big.

Multitemporal analysis of multisensor data: Information theoretical approaches

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.

Speckle reduction in SAR images-techniques and prospects

Presents a classification overview and a comparative study of despeckling algorithms. Almost all of the existing good speckle reduction algorithms show high computational complexity. Promising results giving a very good compromise between image enhancement and algorithm complexity are obtained using multiscale analysis techniques.

Wavelets: a universal tool for the processing of remote sensing data?

During the last years, wavelets have become very popular in the fields of signal processing and pattern recognition and have led to a large number of publications. In the discipline of remote sensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remote sensing image data - both for optical and radar data - follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remote sensing image data. Summarizing potential wavelet applications for remote sensing image data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.