Visualization of mobile mapping data via parallax scrolling

Visualizing big point-clouds, such as those derived from mobile mapping data, is not an easy task. Therefore many approaches have been proposed, based on either reducing the overall amount of data or the amount of data that is currently displayed to the user. Furthermore, an entirely free navigation within such a point-cloud is also not always intuitive using the usual input devices. This work proposes a visualization scheme for massive mobile mapping data inspired by a multiplane camera model also known as parallax scrolling. This technique, albeit entirely two-dimensional, creates a depth illusion by moving a number of overlapping partially transparent image layers at various speeds. The generation of such layered models from mobile mapping data greatly reduces the amount of data up to about 98% depending on the used image resolution. Finally, it is well suited for the panoramic-fashioned visualization of the environment of a moving car

Incorporating independent component analysis and multi-temporal sar techniques to retrieve rapid postseismic deformation

This study investigates the ongoing postseismic deformation induced by two moderate mainshocks of Mw 6.1 and Mw 6.0, 2017 Hojedk earthquake in Southern Iran. Available Sentinel-1 TOPS C-band Synthetic Aperture Radar (SAR) images over about one year after the earthquakes are used to analyze the postseismic activities. An adaptive method incorporating Independent Component Analysis (ICA) and multi-Temporal Small BAseline Subset (SBAS) Interferometric SAR (InSAR) techniques is proposed and implemented to recover the rapid deformation. This method is applied to the series of interferograms generated in a fully constructed SBAS network to retrieve the postseismic deformation signal. ICA algorithm uses a linear transformation to decompose the input mixed signal to its source components, which are non-Gaussian and mutually independent. This analysis allows extracting the low rate postseismic deformation signal from a mixture of interferometric phase components. The independent sources recovered from the multi-Temporal InSAR dataset are then analyzed using a group clustering test aiming to identify and enhance the undescribed deformation signal. Analysis of the processed interferograms indicates a promising performance of the proposed method in determining tectonic deformation. The proposed method works well, mainly when the tectonic signal is dominated by the undesired signals, including atmosphere or orbital/unwrapping noise that counts as temporally uncorrelated components.In contrast to the standard SBAS time series method, the ICA-based time series analysis estimates the cumulative deformation with no prior assumption about elevation dependence of the interferometric phase or temporal nature of the tectonic signal. Application of the method to 433 Sentinel-1 pairs within the dataset reports two distinct deformation patches corresponding to the postseismic deformation. Besides the performance of the ICA-based analysis, the proposed method automatically detects rapid or low rate tectonic processes in unfavorable conditions. © Authors 2020. All rights reserved

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Evaluation of penalty functions for semi-global matching cost aggregation

The stereo matching method semi-global matching (SGM) relies on consistency constraints during the cost aggregation which are enforced by so-called penalty terms. This paper proposes new and evaluates four penalty functions for SGM. Due to mutual dependencies, two types of matching cost calculation, census and rank transform, are considered. Performance is measured using original and degenerated images exhibiting radiometric changes and noise from the Middlebury benchmark. The two best performing penalty functions are inversely proportional and negatively linear to the intensity gradient and perform equally with 6.05 % and 5.91 % average error, respectively. The experiments also show that adaptive penalty terms are mandatory when dealing with difficult imaging conditions. Consequently, for highest algorithmic performance in real-world systems, selection of a suitable penalty function and thorough parametrization with respect to the expected image quality is essential.Stifterverband für die deutsche Wissenschaf

Multi-scale building maps from aerial imagery

Nowadays, the extraction of buildings from aerial imagery is mainly done through deep convolutional neural networks (DCNNs). Buildings are predicted as binary pixel masks and then regularized to polygons. Restricted by nearby occlusions (such as trees), building eaves, and sometimes imperfect imagery data, these results can hardly be used to generate detailed building footprints comparable to authoritative data. Therefore, most products can only be used for mapping at smaller map scale. The level of detail that should be retained is normally determined by the scale parameter in the regularization algorithm. However, this scale information has been already defined in cartography. From existing maps of different scales, neural network can be used to learn such scale information implicitly. The network can perform generalization directly on the mask output and generate multi-scale building maps at once. In this work, a pipeline method is proposed, which can generate multi-scale building maps from aerial imagery directly. We used a land cover classification model to provide the building blobs. With the models pre-trained for cartographic building generalization, blobs were generalized to three target map scales, 1:10,000, 1:15,000, and 1:25,000. After post-processing with vectorization and regularization, multi-scale building maps were generated and then compared with existing authoritative building data qualitatively and quantitatively. In addition, change detection was performed and suggestions for unmapped buildings could be provided at a desired map scale. . © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Feature evaluation for building facade images - an empirical study

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window

Investigations on skip-connections with an additional cosine similarity loss for land cover classification

Pixel-based land cover classification of aerial images is a standard task in remote sensing, whose goal is to identify the physical material of the earth's surface. Recently, most of the well-performing methods rely on encoder-decoder structure based convolutional neural networks (CNN). In the encoder part, many successive convolution and pooling operations are applied to obtain features at a lower spatial resolution, and in the decoder part these features are up-sampled gradually and layer by layer, in order to make predictions in the original spatial resolution. However, the loss of spatial resolution caused by pooling affects the final classification performance negatively, which is compensated by skip-connections between corresponding features in the encoder and the decoder. The most popular ways to combine features are element-wise addition of feature maps and 1x1 convolution. In this work, we investigate skip-connections. We argue that not every skip-connections are equally important. Therefore, we conducted experiments designed to find out which skip-connections are important. Moreover, we propose a new cosine similarity loss function to utilize the relationship of the features of the pixels belonging to the same category inside one mini-batch, i.e.These features should be close in feature space. Our experiments show that the new cosine similarity loss does help the classification. We evaluated our methods using the Vaihingen and Potsdam dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 91.1% for both test sites. © Authors 2020. All rights reserved