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

Superconductivity at 11.3 K induced by cobalt doping in CeOFeAs

Pure phases of a new oxyarsenide superconductor of the nominal composition CeOFe0.9Co0.1As was successfully synthesized by solid state reaction in sealed silica ampoules at 1180 C. It crystallizes in the layered tetragonal ZrCuSiAs type structure (sp gp P4/nmm) with lattice parameter of a = 3.9918(5) angstrom and c = 8.603(1) angstrom. A sharp superconducting transition is observed at 11.31 K with an upper critical field of 45.22 T at ambient pressure. The superconducting transition temperature is drastically lowered (~ 4.5, 4.9 K) on increasing the concentration (x = 0.15, 0.2) of cobalt

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

Local heat-transfer performance and mechanisms in radial flow between parallel disks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76459/1/AIAA-13-213.pd

Evaluating e-portfolio Using by Learning Stages: A Case Study in an Interdisciplinary Program

This study conducts an investigation of posts in the e-portfolio platform of the program: “The interdisciplinary training program for talented college students in science.” Participants in this program were supposed to show their learning portfolios on this platform. Among the 2150 registered students, we randomly selected 126 students who have made at least 3 posts to become the target sample. By identifying the learning stages and posting styles shown by their posts, we find that students are mostly in the surface learning stages and weak in completing their learning portfolios. The results suggest that more strategies should be learned in e-portfolio use. In addition, some related issues about learning performance are also discussed

ESSVCS: an enriched secret sharing visual cryptography

Visual Cryptography (VC) is a powerful technique that combines the notions of perfect ciphers and secret sharing in cryptography with that of raster graphics. A binary image can be divided into shares that are able to be stacked together so as to approximately recover the original image. VC is a unique technique in the sense that the encrypted message can be decrypted directly by the Human Visual System (HVS). The distinguishing characteristic of VC is the ability of secret restoration without the use of computation. However because of restrictions of the HVS, pixel expansion and alignment problems, a VC scheme perhaps can only be applied to share a small size of secret image. In this paper, we present an Enriched Secret Sharing Visual Cryptography Scheme (ESSVCS) to let the VC shares carry more secrets, the technique is to use cypher output of private-key systems as the input random numbers of VC scheme, meanwhile the encryption key could be shared, the shared keys could be associated with the VC shares. After this operation, VC scheme and secret sharing scheme are merged with the private-key system. Under this design, we implement a (k; t; n)-VC scheme. Compared to those existing schemes, our scheme could greatly enhance the ability of current VC schemes and could cope with pretty rich secrets

Marked point processes for the automatic detection of bomb craters in aerial wartime images

Many countries were the target of air strikes during the Second World War. The aftermath of such attacks is felt until today, as numerous unexploded bombs or duds still exist in the ground. Typically, such areas are documented in so-called impact maps, which are based on detected bomb craters. This paper proposes a stochastic approach to automatically detect bomb craters in aerial wartime images that were taken during World War II. In this work, one aspect we investigate is the type of object model for the crater: we compare circles with ellipses. The respective models are embedded in the probabilistic framework of marked point processes. By means of stochastic sampling the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function which describes the conformity with a predefined model. High gradient magnitudes along the border of the object are favoured and overlapping objects are penalized. In addition, a term that requires the grey values inside the object to be homogeneous is investigated. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global optimum of the energy function. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results, based on 22 aerial wartime images, show the general potential of the method for the automated detection of bomb craters and the subsequent automatic generation of an impact map. © Authors 2019

Supraglacial rivers on the northwest Greenland Ice Sheet, Devon Ice Cap, and Barnes Ice Cap mapped using Sentinel-2 imagery

Supraglacial rivers set efficacy and time lags by which surface meltwater is routed to the englacial, subglacial, and proglacial portions of ice masses. However, these hydrologic features remain poorly studied mainly because they are too narrow (typically <30 m) to be reliably delineated in conventional moderate-resolution satellite images (e.g., 30 m Landsat-8 imagery). This study demonstrates the utility of 10 m Sentinel-2 Multi-Spectral Instrument images to map supraglacial rivers on the northwest Greenland Ice Sheet, Devon Ice Cap, and Barnes Ice Cap, covering a total area of ∼10,000 km2. Sentinel-2 and Landsat-8 both capture overall supraglacial drainage patterns, but Sentinel-2 images are superior to Landsat-8 images for delineating narrow and continuous supraglacial rivers. Sentinel-2 mapping across the three study areas reveals a variety of supraglacial drainage patterns. In northwest Greenland near Inglefield Land, subparallel supraglacial rivers up to 55 km long drain meltwater directly off the ice sheet onto the proglacial zone. On the Devon and the Barnes ice caps, shorter supraglacial rivers (up to 15–30 km long) are commonly interrupted by moulins, which drain internally drained catchments on the ice surface to subglacial systems. We conclude that Sentinel-2 offers strong potential for investigating supraglacial meltwater drainage patterns and improving our understanding of the hydrological conditions of ice masses globally