Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications

A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem. The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training. Obtained results, at pixel level segmentation tasks over orthoimages, suggest that SSL enhanced loss functions can be beneficial in models' performance

AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure

Noise-Tolerant Hyperspectral Image Classification Using Discrete Cosine Transform and Convolutional Neural Networks

Hyperspectral image classification has drawn significant attention in the recent years driven by the increasing abundance of sensor-generated hyper- and multi-spectral data, combined with the rapid advancements in the field of machine learning. A vast range of techniques, especially involving deep learning models, have been proposed attaining high levels of classification accuracy. However, many of these approaches significantly deteriorate in performance in the presence of noise in the hyperspectral data. In this paper, we propose a new model that effectively addresses the challenge of noise residing in hyperspectral images. The proposed model, which will be called DCT-CNN, combines the representational power of Convolutional Neural Networks with the noise elimination capabilities introduced by frequency-domain filtering enabled through the Discrete Cosine Transform. In particular, the proposed method entails the transformation of pixel macroblocks to the frequency domain and the discarding of information that corresponds to the higher frequencies in every patch, in which pixel information of abrupt changes and noise often resides. Experiment results in Indian Pines, Salinas and Pavia University datasets indicate that the proposed DCT-CNN constitutes a promising new model for accurate hyperspectral image classification offering robustness to different types of noise, such as Gaussian and salt and pepper noise. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

VEGF increases the permeability of sheep pleura ex vivo through VEGFR2 stimulation

Vascular endothelial growth factor (VEGF), a cytokine that increases vascular permeability to water and proteins and induces angiogenesis, has been implicated in the development of pleural effusions. Inflammatory and malignant pleural effusions are rich in VEGF content while mesothelial cells produce and excrete VEGF. In this report we aimed at investigating by means of electrophysiology the direct effects of VEGF on the parietal and visceral sheep pleura as well as the type of receptors that mediate this effect. Our findings show that VEGF has a direct effect on the pleural mesothelium rendering it more permeable and this effect is mediated through the stimulation of VEGF receptor 2. Our findings shed more light to the role of VEGF in the pathogenesis of pleural effusions and provide functional evidence for a role of VEGFR2 on the pleural mesothelium that has never been studied before. (C) 2014 Elsevier Ltd. All rights reserved