Multi-source multi-scale hierarchical conditional random field model for remote sensing image classification

Fusion of remote sensing images and LiDAR data provides complimentary information for the remote sensing applications, such as object classification and recognition. In this paper, we propose a novel multi-source multi-scale hierarchical conditional random field (MSMSH-CRF) model to integrate features extracted from remote sensing images and LiDAR point cloud data for image classification. MSMSH-CRF model is then constructed to exploit the features, category compatibility of multi-scale images and the category consistency of multi-source data based on the regions. The output of the model represents the optimal results of the image classification. We have evaluated the precision and robustness of the proposed method on airborne data, which shows that the proposed method outperforms standard CRF method.National Natural Science Fund of China/4090117

Towards Automated Cadastral Boundary Delineation from UAV data

This PhD research aims to design and implement a method to facilitate land rights mapping through indirect surveying techniques from UAV data. It is based on the assumption that a large portion of cadastral boundaries is physically manifested through objects such as hedges, fences, stone walls, tree lines, roads, walkways or waterways. Those visible boundaries bear the potential to be extracted with methods from photogrammetry, remote sensing and computer vision. The automatically extracted outlines require further (legal) adjudication that allows incorporating local knowledge from a human operator. The method currently being designed and developed within this PhD research aims to provide a delineation approach that includes this automated extraction combined with an interactive delineation (Figure 1). This work is part of the Horizon 2020 program of the European Union (project its4land)

Towards better classification of land cover and land use based on convolutional neural networks

Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%. © Authors 2019

Quantification of the boron speciation in alkali borosilicate glasses by electron energy loss spectroscopy

Transmission electron microscopy and related analytical techniques have been widely used to study the microstructure of different materials. However, few research works have been performed in the field of glasses, possibly due to the electron-beam irradiation damage. In this paper, we have developed a method based on electron energy loss spectroscopy (EELS) data acquisition and analyses, which enables determination of the boron speciation in a series of ternary alkali borosilicate glasses with constant molar ratios. A script for the fast acquisition of EELS has been designed, from which the fraction of BO(4) tetrahedra can be obtained by fitting the experimental data with linear combinations of the reference spectra. The BO(4) fractions (N(4)) obtained by EELS are consistent with those from (11)B MAS NMR spectra, suggesting that EELS can be an alternative and convenient way to determine the N(4) fraction in glasses. In addition, the boron speciation of a CeO(2) doped potassium borosilicate glass has been analyzed by using the time-resolved EELS spectra. The results clearly demonstrate that the BO(4) to BO(3) transformation induced by the electron beam irradiation can be efficiently suppressed by doping CeO(2) to the borosilicate glasses

Vision-based indoor localization via a visual slam approach

With an increasing interest in indoor location based services, vision-based indoor localization techniques have attracted many attentions from both academia and industry. Inspired by the development of simultaneous localization and mapping technique (SLAM), we present a visual SLAM-based approach to achieve a 6 degrees of freedom (DoF) pose in indoor environment. Firstly, the indoor scene is explored by a keyframe-based global mapping technique, which generates a database from a sequence of images covering the entire scene. After the exploration, a feature vocabulary tree is trained for accelerating feature matching in the image retrieval phase, and the spatial structures obtained from the keyframes are stored. Instead of querying by a single image, a short sequence of images in the query site are used to extract both features and their relative poses, which is a local visual SLAM procedure. The relative poses of query images provide a pose graph-based geometric constraint which is used to assess the validity of image retrieval results. The final positioning result is obtained by selecting the pose of the first correct corresponding image. © Authors 2019