Refined Building Change Detection in Satellite Stereo Imagery Based on Belief Functions and Reliabilities

Digital Surface Models (DSMs) generated from satellite stereo imagery provide valuable but not comprehensive information for building change detection. Therefore, belief functions have been introduced to solve this problem by fusing DSM information with changes extracted from images. However, miss-detection can not be avoided if the DSMs are containing large region of wrong height values. A refined workflow is thereby proposed by adopting the initial disparity map to generate a reliability map. This reliability map is then built in the fusion model. The reliability map has been tested in both Dempster-Shafer Theory (DST), and Dezert-Smarandache Theory (DSmT) frameworks. The results have been validated by comparing to the manually extracted change reference mask

If . . .: Counterfactuals in the Law

This Article considers some of the uses of counterfactuals in the law. Counterfactuals are a type of conditional statement. Conditional statements express the idea that something is or will be the case (the consequent), provided that some other situation is realized (the antecedent). Conditionals often take the form if p then q , Counterfactuals are conditionals in which the author expresses the knowledge or belief that the antecedent is false

Prediction of Wind Speeds based on Digital Elevation Models using Boosted Regression Trees

In this paper a new approach is presented to predict maximum wind speeds using Gradient Boosted Regression Trees (GBRT). GBRT are a non-parametric regression technique used in various applications, suitable to make predictions without having an in-depth a-priori knowledge about the functional dependancies between the predictors and the response variables. Our aim is to predict maximum wind speeds based on predictors, which are derived from a digital elevation model (DEM). The predictors describe the orography of the Area-of-Interest (AoI) by various means like first and second order derivatives of the DEM, but also higher sophisticated classifications describing exposure and shelterness of the terrain to wind flux. In order to take the different scales into account which probably influence the streams and turbulences of wind flow over complex terrain, the predictors are computed on different spatial resolutions ranging from 30 m up to 2000 m. The geographic area used for examination of the approach is Switzerland, a mountainious region in the heart of europe, dominated by the alps, but also covering large valleys. The full workflow is described in this paper, which consists of data preparation using image processing techniques, model training using a state-of-the-art machine learning algorithm, in-depth analysis of the trained model, validation of the model and application of the model to generate a wind speed map

Geometric Evaluation of Gaofen-7 Stereo Data

China's first sub-metre stereo satellite, GaoFen-7, was launched on 7 November 2019. One of the main criteria for a stereo mapping satellite is the geometric accuracy of the images. In this paper, we present a systematic evaluation of the geometry accuracy of Gaofen-7 on two scenes over the centre of Munich, Germany. The geometry accuracy is evaluated in a three-step workflow: 1) direct georeferencing accuracy; 2) image orientation using bundle adjustment with ground control points; 3) height accuracy of the generated digital surface model (DSM). In addition to dense LiDAR point clouds, ground control points were measured in the field. These were used as references. The results show that RPC bundle adjustment with 0 order bias correction is sufficient to achieve sub-metre absolute accuracy. The height accuracy of the generated digital surface models varies with land cover type, ranging from 0.9m (NMAD) in open areas to 4.5m in urban areas

Multimodal Co-learning: A Domain Adaptation Method for Building Extraction from Optical Remote Sensing Imagery

In this paper, we aim to improve the transfer learning ability of 2D convolutional neural networks (CNNs) for building extraction from optical imagery and digital surface models (DSMs) using a 2D-3D co-learning framework. Unlabeled target domain data are incorporated as unlabeled training data pairs to optimize the training procedure. Our framework adaptively transfers unsupervised mutual information between the 2D and 3D modality (i.e., DSM-derived point clouds) during the training phase via a soft connection, utilizing a predefined loss function. Experimental results from a spaceborne-to-airborne cross-domain case demonstrate that the framework we present can quantitatively and qualitatively improve the testing results for building extraction from single-modality optical images

Multi-label learning based semi-global matching forest

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently

HPLC/QTOF-MS metabolomics analysis applied to identify skin biomarkers of UVC-induced skin injury in mice and preventive effects of abietic acid

Abietic acid (AA) is a main constituent from pine resin, which has definite therapeutical effects for treating skin ulcers and tumor. Here, we explored the metabolome changes in skin tissues of mice with UVC-induced skin injury treated with AA by a HPLC-QTOF-MS/MS method. Model mice were induced with UVC irradiation. Skin histopathological changes were examined by routine HE staining. Metabolomic analysis technology and pattern recognition statistical method were applied to analyze the metabolites in the skin tissues of mice to study the therapeutic effect of AA on UVC-induced skin injury in mice. Ceramides, sphingosines, glycyl-L-glutamine, dihydroorotic acid, adenosine, dCMP and lyso-phosphatidylcholines can be used as biomarkers of UVC-induced skin injury. AA can improve the pathological tissue from the pathway of skin lipid and purine pyrimidine metabolism to achieve the therapeutic effect. AA can effectively treat UVC-induced skin injury in mice

Cross field-based segmentation and learning-based vectorization for rectangular windows

Detection and vectorization of windows from building façades are important for building energy modeling, civil engineering, and architecture design. However, current applications still face the challenges of low accuracy and lack of automation. In this paper we propose a new two-steps workflow for window segmentation and vectorization from façade images. First, we propose a cross field learning-based neural network architecture, which is augmented by a grid-based self-attention module for window segmentation from rectified façade images, resulting in pixel-wise window blobs. Second, we propose a regression neural network augmented by Squeeze-and-Excitation (SE) attention blocks for window vectorization. The network takes the segmentation results together with the original façade image as input, and directly outputs the position of window corners, resulting in vectorized window objects with improved accuracy. In order to validate the effectiveness of our method, experiments are carried out on four public façades image datasets, with results usually yielding a higher accuracy for the final window prediction in comparison to baseline methods on four datasets in terms of IoU score, F1 score, and pixel accuracy