Search

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m$^2$). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identification, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A. [ABSTRACT FROM PUBLISHER]

Satellite remote sensing missions devoted to Earth observation (EO) currently offer a unique capability to monitor the evolution of the Earth's surface by providing temporally repetitive views at the desired (global, regional, or local) spatial scale. This wealth of remote sensing data conveys a huge potential for preventing, monitoring, and managing natural or man-made disasters. Specifically focusing on flood risk, a successful exploitation of this potential requires not only accurate and reliable image-analysis methods to extract the desired thematic information, but also the ability to combine this information with physically based models of the observed processes. Therefore, a multidisciplinary approach combining remote sensing with geophysical sciences, such as, in this case, hydrometeorology, is fundamental. This combination of expertise allows, in particular, satellite data to be exploited within the different phases of flood risk reduction: risk assessment, prevention, mitigation, monitoring, and management. In this paper, we investigate the key issues involved in the exploitation of satellite data with special focus on the phases of the emergency and post-disaster damage assessment. To this end, the challenges and the methodological approaches involved in the multidisciplinary combination of image analysis and hydrometeorology are discussed with the purpose of guiding and optimizing the process of information extraction from satellite data according to the requirements of civil protection from floods. Experimental examples of a few relevant case studies are also presented. [ABSTRACT FROM AUTHOR]

Multichannel synthetic aperture radar (SAR) data present a good potential for environmental monitoring and disaster management, owing both to their insensitivity to atmospheric and sun-illumination conditions, and to the improved discrimination capability they may provide as compared to single-channel SAR. However, this requires accurate and possibly automatic techniques to generate change maps from multichannel SAR images acquired from the same geographic area at different times. In this letter, an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with a variant of the expectation-maximization algorithm and with Markov random fields. The method is validated by experiments on SIR-C/XSAR data [ABSTRACT FROM PUBLISHER]