Network-Based Continuous Space Representation for Describing Pedestrian Movement in High Resolution

A concept of network-based continuous space representation is proposed and applied to the sequential map matching problem with simulation data assuming pedestrian movement. The concept allows for dealing with situations that the resolution of network representation is not high enough to describe the pedestrian movement considering the observation accuracy. The experiment showed that the proposed concept worked well in the example of pedestrian movement along with the sidewalk by estimation of accurate positions

Electric Dipolar Susceptibility of the Anderson-Holstein Model

The temperature dependence of electric dipolar susceptibility \chi_P is discussed on the basis of the Anderson-Holstein model with the use of a numerical renormalization group (NRG) technique. Note that P is related with phonon Green's function D. In order to obtain correct temperature dependence of P at low temperatures, we propose a method to evaluate P through the Dyson equation from charge susceptibility \chi_c calculated by the NRG, in contrast to the direct NRG calculation of D. We find that the irreducible charge susceptibility estimated from \chi_c agree with the perturbation calculation, suggesting that our method works well.Comment: 4 pages, 4 figure

Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles

The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving deep learning based semantic segmentation with multi view outliner correction

The goal of this paper is to use transfer learning for semi supervised semantic segmentation in 2D images: given a pretrained deep convolutional network (DCNN), our aim is to adapt it to a new camera-sensor system by enforcing predictions to be consistent for the same object in space. This is enabled by projecting 3D object points into multi-view 2D images. Since every 3D object point is usually mapped to a number of 2D images, each of which undergoes a pixelwise classification using the pretrained DCNN, we obtain a number of predictions (labels) for the same object point. This makes it possible to detect and correct outlier predictions. Ultimately, we retrain the DCNN on the corrected dataset in order to adapt the network to the new input data. We demonstrate the effectiveness of our approach on a mobile mapping dataset containing over 10'000 images and more than 1 billion 3D points. Moreover, we manually annotated a subset of the mobile mapping images and show that we were able to rise the mean intersection over union (mIoU) by approximately 10% with Deeplabv3+, using our approach. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving disparity estimation based on residual cost volume and reconstruction error volume

Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks. However, most resulting pixel-wise disparity maps only show little detail for small structures. In this paper, we propose a two-stage architecture: we first learn initial disparities using an initial network, and then employ a disparity refinement network, guided by the initial results, which directly learns disparity corrections. Based on the initial disparities, we construct a residual cost volume between shared left and right feature maps in a potential disparity residual interval, which can capture more detailed context information. Then, the right feature map is warped with the initial disparity and a reconstruction error volume is constructed between the warped right feature map and the original left feature map, which provides a measure of correctness of the initial disparities. The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. We use a shallow encoder-decoder module in the refinement network and do learning from coarse to fine, which simplifies the learning problem. We evaluate our method on several challenging stereo datasets. Experimental results demonstrate that our refinement network can significantly improve the overall accuracy by reducing the estimation error by 30% compared with our initial network. Moreover, our network also achieves competitive performance compared with other CNN-based methods. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Improving the classification of Land use Objects using Dense Connectitvity of Convolutional Neural Networks

Land use is an important variable in remote sensing which describes the functions carried out on a piece of land in order to obtain benefits and is especially useful to the personnel working in the fields of urban management and planning. The land use information is maintained by national mapping agencies in geo-spatial databases. Commonly, land use data is stored in the form of polygon objects; the label of the object indicates land use. The main goal of classification of land use objects is to update an existing database in an automatic process. Recently, Convolutional Neural Networks (CNN) have been widely used to tackle this task utilizing high resolution aerial images (and derived data such as digital surface model). One big challenge classifying polygons is to deal with the large variation in their geometrical extent. For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size. The decomposition leads to two sets of polygons: small and large, where the former suffers from a lower identification rate. In this paper, we propose CNN methods which incorporate dense connectivity and integrate it with intermediate information via global average pooling to improve land use classification, mainly focusing on small polygons. We present different network variants by incorporating intermediate information via global average pooling from different stages of the network. We test our methods on two sites; our experiments show that the dense connectivity and integration of intermediate information has a positive effect not only on the classification accuracy on the whole but also on the identification of small polygons. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

The Hayabusa Spacecraft Asteroid Multi-Band Imaging Camera: AMICA

The Hayabusa Spacecraft Asteroid Multiband Imaging Camera (AMICA) has acquired more than 1400 multispectral and high-resolution images of its target asteroid, 25143 Itokawa, since late August 2005. In this paper, we summarize the design and performance of AMICA. In addition, we describe the calibration methods, assumptions, and models, based on measurements. Major calibration steps include corrections for linearity and modeling and subtraction of bias, dark current, read-out smear, and pixel-to-pixel responsivity variations. AMICA v-band data were calibrated to radiance using in-flight stellar observations. The other band data were calibrated to reflectance by comparing them to ground-based observations to avoid the uncertainty of the solar irradiation in those bands. We found that the AMICA signal was linear with respect to the input signal to an accuracy of << 1% when the signal level was < 3800 DN. We verified that the absolute radiance calibration of the AMICA v-band (0.55 micron) was accurate to 4% or less, the accuracy of the disk-integrated spectra with respect to the AMICA v-band was about 1%, and the pixel-to-pixel responsivity (flatfield) variation was 3% or less. The uncertainty in background zero-level was 5 DN. From wide-band observations of star clusters, we found that the AMICA optics have an effective focal length of 120.80 \pm 0.03 mm, yielding a field-of-view (FOV) of 5.83 deg x 5.69 deg. The resulting geometric distortion model was accurate to within a third of a pixel. We demonstrated an image-restoration technique using the point-spread functions of stars, and confirmed that the technique functions well in all loss-less images. An artifact not corrected by this calibration is scattered light associated with bright disks in the FOV.Comment: 107 pages, 22 figures, 9 tables. will appear in Icaru