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This paper presents two change-detection strategies based on the fusion of scene knowledge and two high-resolution synthetic aperture radar (SAR) images (pre-event, postevent) with focus on individual buildings and facades. Avoiding the dependence of the signal incidence angle, the methods increase the flexibility with respect to near-real-time SAR image analysis after unexpected events. Knowledge of the scene geometry is provided by digital surface models (DSMs), which are integrated into an automated simulation processing chain. Using strategy 1 (based on building fill ratio, BFR), building changes are detected based on change ratios considering layover and shadow areas. Strategy 2 (based on wall fill position, WFP) enables one to analyze individual facades of buildings without clear decision from strategy 1, which is based on a geometric projection of facade layover pixels. In a case study (Munich city center), the sensitivity of the change-detection methods is exemplified with respect to destroyed buildings and partly changed buildings. The results confirm the significance of integrating prior knowledge from DSMs into the analysis of high-resolution SAR images.

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.

This paper presents synthetic aperture radar (SAR) image categorization based on feature descriptors within the discrete wavelet transform (DWT) domain using nonparametric and parametric features. The first and second moments, Kolmogorov Sinai entropy and coding gain, are used for the nonparametric features within an oriented dual tree complex wavelet transform (2D ODT $\mathbb{C}$ WT). A Gauss–Markov random field (GMRF), triplet Markov random field (TMRF), and autobinomial model (ABM) are used for feature extraction using a parametric approach within an image domain. A single parameter of GMRF, TMRF, or ABM is used for characterizing an entire patch; therefore, higher model orders (MOs) are used. A database with 2000 images representing 20 different classes with 100 images per class is used for estimating classification efficiency. A supervised learning stage is implemented within a support vector machine (SVM) using 10% and 20% of the test images per class. The experimental results showed that the nonparametric features achieved better results when compared to the parametric features.

The amount of collected Earth Observation (EO) data is increasing immensely with a rate of several Terabytes of data per day. Simultaneously with this increasing of data, new trends for exploration and information retrieval are highly needed. In the last year, the proposed method tries to explore the EO data using Image Information Mining (IIM) approach in which primitive feature extraction and classification are the main steps, developing a new process chain and a new taxonomy for the retrieved categories, mainly based on human interaction, can be a good solution. This paper proposes to explore the content of images and to identify the number of objects and land cover categories that can be retrieved from high resolution TerraSAR-X data. We need to mention that is for the first time when for remote sensing a large data set (e.g., TerraSAR-X images) covering different cities over the world is annotated and for each category a taxonomy for high resolution data is defined. Applications that may result from this study can be a semantic catalogue for TerraSAR-X, urban crisis, disasters, etc. First, we strongly need an automatic or semi-automatic searching tool capable to find in large EO data set similar sub-images (i.e. patches) and to group them in categories. Secondary, we need to define a taxonomy that can be used to semantically annotate each category using the human interaction. The data set consists of 109 scenes that cover different areas over the world: 5 scenes from Africa, 27 scenes from Asia, 44 scenes from Europe, 11 scenes from Middle East, and 22 scenes from North and South of America. These scenes are grouped in collections based on three criteria in order: (1) to get an idea about how many categories can be retrieved for each city/country/continent, (2) to see whether the same urban categories belong to two different scenes, and (3) to help us to annotate large data. Data set is grouped in more collections using previous three criteria and each collection is process separately using an enhanced methodology that take the scene/scenes and tile them into patches. Gabor filters is used as a primitive features method and is applied to each patch. Support vector machine with relevance feedback is implemented in order to group the features in categories of relevance. Finally, these categories are semantically annotated using as ground truth the Google Earth. In our investigation more than 850 categories were retrieved with their specific taxonomy. The novelty of this paper lies in the fact that this is the first time when a semantic annotation was made on a large number of scenes containing high resolution synthetic aperture radar images. This investigation has an important impact in applications such as classification of urban areas, infrastructure (e.g., airport, port, etc.), geography images (e.g., mountains, etc.), industrial sites, military sites, vegetation, and agriculture. The proposed taxonomies can be the basis for building a semantic catalogue for EO images. Finally, four types of query are defined and are planned to be integrated into the new system developed at DLR. The query provides opportunities to EO users to search into the database for some specific parameters or semantic of the existing data set.

Spectral similarities and spatial adjacencies between various kinds of objects, shadow, and occluded areas behind high-rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in object recognition in urban areas. Using a knowledge base containing the contextual information together with the multiviews imagery may improve the object recognition results in such a situation. The proposed object recognition strategy in this paper has two main stages: single view and multiviews processes. In the single view process, defining region’s properties for each of the segmented regions, the object-based image analysis (OBIA) is performed independently on the individual views. In the second stage, the classified objects of all views are fused together through a decision-level fusion based on the scene contextual information in order to refine the classification results. Sensory information, analyzing visibility maps, height, and the structural characteristics of the multiviews classified objects define the scene contextual information. Evaluation of the capabilities of the proposed context aware object recognition methodology is performed on two datasets: 1) multiangular Worldview-2 satellite images over Rio de Janeiro in Brazil and 2) multiviews digital modular camera (DMC) aerial images over a complex urban area in Germany. The obtained results represent that using the contextual information together with a decision-level fusion of multiviews, the object recognition difficulties and ambiguities are decreased and the overall accuracy and the kappa are gradually improved for both of the WorldView-2 and the DMC datasets.

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.