Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Automatic Generation of Training Data for Land Use and Land Cover Classification by Fusing Heterogeneous Data Sets

2020 , Schmitz, Sylvia , Weinmann, Martin , Weidner, Uwe , Hammer, Horst , Thiele, Antje

Nowadays, automatic classification of remote sensing data can efficiently produce maps of land use and land cover, which provide an essential source of information in the field of environmental sciences. Most state-of-the-art algorithms use supervised learning methods that require a large amount of annotated training data. In order to avoid time-consuming manual labelling, we propose a method for the automatic annotation of remote sensing data that relies on available land use and land cover information. Using the example of automatic labelling of SAR data, we show how the Dempster-Shafer evidence theory can be used to fuse information from different land use and land cover products into one training data set. Our results confirm that the combination of information from OpenStreetMap, CORINE Land Cover 2018, Global Surface Water and the SAR data itself leads to reliable class assignments, and that this combination outperforms each considered single land use and land cover product.

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A comparative study of coherence estimators for interferometric SAR image coregistration and coherent change detection

2019 , Hammer, Horst , Thiele, Antje , Lorenz, Fabian , Cadario, Erich , Kuny, Silvia , Schulz, Karsten

The coherent nature of the SAR imaging process opens up the opportunity to create interferometric image pairs, which carry a large amount of information about the scene. In this paper, the interferometric coherence is investigated in detail. Coherence is a measure for the temporal stability of the scene with respect to the phase information. Classically, coherence is used for the task of co-registration of the image pair, with the goal of coherence maximization, since such a co-registration will yield the most reliable interferometric phase information. The second important field of application is coherent change detection, i.e. the detection of changes in the scene that most often do not change the backscattering properties of the images and thus are not detectable in the amplitude images. For such an application it is of importance to maximize the contrast between the incoherent changed parts of the scene and the coherent surroundings. With these two applications in mind, in this paper, several published coherence estimation schemes are investigated. The different coherence estimators are applied to an airborne data set, and results regarding coherence maximization and coherence contrast maximization are shown.

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Incorporating interferometric coherence into LULC classification of airborne PolSAR-images using fully convolutional networks

2020 , Schmitz, Sylvia , Weinmann, Martin , Thiele, Antje

Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. The results demonstrate that the proposed model produces smooth and accurate classification maps. A comparison with a single-branch FCN model indicates that the appropriate integration of interferometric coherence enables the improvement of classification performance.

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Practical approach for synthetic aperture radar change analysis in urban environments

2019 , Boldt, Markus , Thiele, Antje , Schulz, Karsten , Meyer, Franz J. , Hinz, Stefan

Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust.

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