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Summary Main aim of this paper is to compare the classification accuracies of glacier change detection by following classifiers: sub-pixel classification algorithm, indices based supervised classification and object based algorithm using Landsat imageries. It was observed that shadow effect was not removed in sub-pixel based classification which was removed by the indices method. Further the accuracy was improved by object based classification. Objective of the paper is to analyse different classification algorithms and interpret which one gives the best results in mountainous regions. The study showed that object based method was best in mountainous regions as optimum results were obtained in the shadowed covered regions.

In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl´eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

The grammar of facade structures is often related to regularly distributed signature patterns in high-resolution synthetic aperture radar (SAR) images. Given those patterns in the imagery, they should be used as a source of information for identifying changes related to the facade. This paper presents a method for characterizing the layover area pertinent to regularly arranged facade structures, formulated on a general basis for single azimuth/range SAR images and geocoded SAR images. The analysis follows assumptions on the intensity distribution, the linear arrangement, and the regularity of point-like signatures. Two case studies on facades are presented, which confirm the applicability of the method for different building types. Based on that, the potentials and limitations of the algorithm are discussed with respect to applications such as change detection and persistent scatterer interferometry.