- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) - Beijing (2018.5.20-2018.5.22)] 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) - Remote Detection and Classification of Human Stress Using a Depth Sensing Technique
摘要： Stress plays an important role in our daily life. Long-term’s psychological stress will lead to serious health as well as social problems, it is important to detect and monitor the psychological stress in its early stage. Most existing stress detection equipment are contact-type, such as wrist strap. However, in a real application, such as a working environment, a system will bring greater contact-free convenience. In this paper, we proposed a novel framework for detecting and classifying human stress based on respiratory signals measured remotely by using a Kinect sensor with a detection range of 3 meters. We test the framework on respiratory signals data set from 20 individuals under 3 different tasks (listen relax music, do exercise and do Stroop Color-word test), corresponding to relaxation, physical stress and psychological stress state. Experimental results suggest that the proposed method is a promising way for monitoring human stress and even discriminating psychological stress from the physical stress.
关键词： stress detection,physiological features of stress,remote sensing respiration signal,stress classification
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Power Transmission Lines Inspection using Properly Equipped Unmanned Aerial Vehicle (UAV)
摘要： The inspection of power transmission lines is an important task that enhances the reliability of Electricity Distribution Network Operators. This task can be performed in a low-cost way using unmanned aircrafts. At the present study, we examine the effectiveness of using basic image processing methods on image data of the power lines acquired by an unmanned aerial vehicle (UAV). The specific UAV was assembled for the present work under the considerations that arise from the purpose of the inspection of power transmission lines. Two methodologies are proposed differing on the pre- processing required in order to detect the location of the lines on the video images. Both proposed methodologies were tested in real-world cases, with the image background in each case to be characterized of non-uniform texture, i.e. the natural terrain is rugged at some locations, wooded land at some other or it is road that appears at the same hue as the aerial power lines. We examined the case of a broken line where the methodologies result in successful detection of the power lines before and after the discontinuity of the power line. The proposed work offers a robust and low-cost way for the inspection of power transmission lines and so an effective way to detect the location where a cable fault has occurred.
关键词： Hough transform,aerial video processing,UAV remote sensing,power line inspection,parametric training
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Context-Aware and Depthwise-based Detection on Orbit for Remote Sensing Image
摘要： Automatic detection on orbit is an efficient way to filter useless data downloaded to the ground. However, detection on orbit is a challenging task due to limited computational resources on the satellite. In this paper, a context-aware and depthwise-based detection framework for remote sensing images is proposed which can be used on orbit. In the result of limited computational resources on the satellite, on-orbit object detection should detect with low memory cost and fast speed while ensuring the accuracy. To address the problem of small model in the process of feature extracting, a depthwise convolution is applied instead of typical convolution. In this light, a small deep neural network is built to run on orbit, using Single Shot Multibox Detector (SSD) as basic detection module. Motivated by its weak performance on remote sensing image owing to few pixel about target object, context information about target object is added to improve performance. To further investigate the context information influence, we add a balance factor to balance the context information and background noise it brings. Then an experiment on real remote sensing image dataset is conducted comparing our extended model with other current state-of-the-art detection models. Results show our extended model outperforms other models in accuracy and speed. Deploying the pretrained model on the Android Platform with only 60M memory cost confirms the feasibility to detect on orbit. This detection system is to be verified on the TZ-1 satellite which will be launched in the year of 2018.
关键词： context-aware,Automatic detection,SSD,remote sensing images,depthwise-based
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Learning Hyperspectral Image Classification using Multiple Class-Based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
摘要： Herein, we present a system for hyperspectral image segmentation that utilizes multiple class–based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.
关键词： remote sensing,Deep learning,denoising autoencoder
更新于2025-09-09 09:28:46
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Analysis of the Effect of Soil Roughness in the Forward-Scattering Interference Pattern Using Second-Order Small Perturbation Method Simulations
摘要： Soil moisture (SM) is a key geophysical variable that can be estimated at regional scales using remote sensing techniques, by making use of the known relationship between soil re?ectivity and the dielectric constant in the microwave regime. In this context, the exploitation of available illuminators of opportunity that currently emit large amounts of power at microwave frequencies (compared to typical synthetic aperture radar systems) is promising. Some published techniques estimate SM by analyzing the interference pattern (IP) between direct and re?ected signal as measured by a single antenna (i.e., IP technique). In this letter, a new approach to simulate the IP is proposed, in which the soil roughness is modeled straightforwardly using the second-order small perturbation model. Results illustrate that the “notch” in the VV-polarization IP (related to the Brewster angle) can only be directly observed for very low values of soil rms roughness (s < 0.5 cm). For typical values of soil roughness (s ～ 1.2 cm), the notch disappears and only a minimum in the IP is observed near the Brewster angle.
关键词： Electromagnetic and remote sensing,microwave radiometry,Global Navigation Satellite System data,surface and subsurface properties
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
摘要： This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
关键词： deep-learning,Landsat 8,FCN,image segmentation,U-Net,remote sensing,CNN,Cloud detection
更新于2025-09-09 09:28:46
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Improving the Accuracy of Near Real-Time Seismic Loss Estimation Using Post-Earthquake Remote Sensing Images
摘要： With the rapid development of remote sensing technology, satellite or aerial images from the disaster area become available within 24 hours after an earthquake. The collapsed buildings can be easily identified from these images. In this work, a framework for near real-time seismic loss estimation for regional buildings is proposed, which improves the accuracy of nonlinear time-history analysis (THA)-based loss estimations by taking advantage of the identified building collapse scene of the disaster area. Specifically, a series of THA are performed for the target regional buildings, thereby generating a number of simulation results. Those simulation results that bear strong similarities to the identified collapse scene are identified as the optimal solutions, which will be used to estimate the seismic loss. The simulation results of the case studies signify that the use of the identified building collapse scene leads to much closer estimations to actual economic losses.
关键词： building collapse,remote sensing,disaster area,seismic loss estimation,nonlinear time-history analysis
更新于2025-09-09 09:28:46
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Pulse-to-Pulse Correlation Effects in High PRF Low-Resolution Mode Altimeters
摘要： In this paper, we revisit the pulse-to-pulse correlation properties of nadir-looking pulse-limited altimeters, with the objective of determining the effect of the partial correlation of radar echoes transmitted at much higher rate than the conventional pulse repetition frequency (PRF). This is particularly relevant for the Sentinel-6/Jason-CS mission. The pulse-to-pulse echo power autocorrelation shows much shorter decorrelation times toward the trailing edge of the waveform than those observed for range gates close to the leading edge. At high PRFs this creates a significant variability in the statistical properties of the range gates in the 20-Hz multilooked waveforms. By processing an extensive data set of CryoSat-2 Synthetic Aperture Radar mode data in a pseudo-low resolution mode fashion, we determined that despite the fact that at higher PRFs the noise in the estimation of geophysical parameters is reduced, significant sea-state-dependent biases are also introduced during the retracking process, which are particularly relevant for sea surface height and significant wave height. Those biases will need to be appropriately accounted for when integrating Sentinel-6/Jason-CS data in a climatological data record.
关键词： remote sensing,sea measurements,radar altimetry,Altimetry,sea surface height (SSH)
更新于2025-09-09 09:28:46
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PEMANFAATAN CITRA SATELIT UNTUK PENENTUAN LAHAN KRITIS MANGROVE DI KECAMATAN TUGU, KOTA SEMARANG
摘要： This study aims to mapping the level of degraded land of mangrove forest area In TUGU Sub-district, Semarang, by comparing the results between the Landsat 7 ETM + images of 2009 and ALOS AVNIR-2 in 2009. In determining the degradation of mangrove forest area, we used geographic information systems and remote sensing as a tool of analysis that is based on three (3) criteria; land use type, canopy density, and soil resilience from abrasion. From 2 satellite image data used, it will be supervised image classification using ER Mapper software to get the criteria type of land use and density of the canopy. For soil resilience from abrasion, we used soil types reclassification techniques, using ArcGIS software. Based on Landsat imagery, obtained results 92.22 % of mangrove forest area included in severely damaged condition and 7.78% is included in the category of moderate damage. Meanwhile, based on the results of ALOS image, 77.73 % of mangrove areas in severely damaged condition and 22.27 % are included in the category of moderate damage. From this study, it can be concluded that ALOS and Landsat Imagery is good for the determination and identifying critical mangrove area and distribution of mangrove forests, but the degraded land of mangrove maps generated by Landsat, less detailed than ALOS in classification and representation the conditions of critical mangrove area in Tugu sub-district.
关键词： Satellite Imagery,GIS,Mangrove,Critical Land,Remote Sensing
更新于2025-09-04 15:30:14
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Análisis bibliométrico mundial de la categoría 'remote sensing' de la web of science (1997-2012)
摘要： This study aimed at analyzing the evolution of the research into 'Remote Sensing' subject category; as a first approximation, we have revised in the Web of Science, the journals that are in that category. Secondly, we used bibliometric indicators to analyze publications for journals between 1997 and 2012. To do so, we analyzed for different countries and research centers, various bibliometric indicators such as the number of documents, showing, the productivity, the average number of citations, authors, research centers, national and international collaboration per document including their networks, the weighted and relative impact factor, as well as the h-index. Furthermore, we analyzed the international dissemination of research of countries through journals and the relationship with the impact factor to detect the published journals of each country. We have shown that English is the most common language of publication, and the USA is the most productive country, although it has a relatively low impact factor. We can remark that the Chinese Academy of Sciences and the National Aeronautics and Space Administration (NASA) are the most productive institutions, and the great number of publications of some Chinese universities.
关键词： ‘Remote Sensing’ subject category,Bibliometric,Science Citation Index-Expanded,Scientific Production,Journal Citation Reports
更新于2025-09-04 15:30:14