The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest.