- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Odessa, Ukraine (2018.9.4-2018.9.7)] 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Active Aperture Synthesis Radar for High Spatial Resolution Imaging
摘要： To solve problems of high-precision mapping of spatially extended objects regardless of weather conditions and time of day, radar imaging systems are used. The globality and efficiency of the survey is provided by the placement of radars on aerospace carriers. At the same time, there has been a proliferation of side-scan radars (providing a wide viewing range with a low spatial resolution) and antenna aperture synthesis radars (these provide high resolution in spatial coordinates, which depends on the type of survey). Radars used for imaging are classified according to various characteristics determining their advantages and disadvantages. Among such features, we can also distinguish the radar viewing area, which is currently limited to angles of 15° to 60° to the right and left of the observation in the nadir. The viewing range from -15° to +15° from the nadir is characterized by a low resolution in range and traditionally is not visible by these radars (the so-called "blind zone"). In this regard, an alternative system for building radar images from aerospace carriers is proposed. It will provide imaging with high angular resolution of the viewing range from -15° to +15° from the nadir. The main feature of the developed radar lies in the signal processing algorithm that combines methods of active (the presence of probing UWB signal) and passive (aperture synthesis) radiolocation. In addition, the radar implements a new method of "spectral aperture synthesis," which allows from processing of UWB signals with the continuum spectrum to processing multi-band signal processing.
关键词： radar mapping,active aperture synthesis,aerospace systems of remote sensing,radar imaging methods
更新于2025-09-23 15:21:21
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Remote Sensing Image Classification Using the Spectral-Spatial Distance Based on Information Content
摘要： Among many types of efforts to improve the accuracy of remote sensing image classification, using spatial information is an effective strategy. The classification method integrates spatial information into spectral information, which is called the spectral-spatial classification approach, has better performance than traditional classification methods. Construct spectral-spatial distance used for classification is a common method to combine the spatial and spectral information. In order to improve the performance of spectral-spatial classification based on spectral-spatial distance, we introduce the information content (IC) in which two pixels are shared to measure spatial relation between them and propose a novel spectral-spatial distance measure method. The IC of two pixels shared was computed from the hierarchical tree constructed by the statistical region merging (SRM) segmentation. The distance we proposed was applied in two distance-based contextual classifiers, the k-nearest neighbors-statistical region merging (k-NN-SRM) and optimum-path forest-statistical region merging (OPF-SRM), to obtain two new contextual classifiers, the k-NN-SRM-IC and OPF-SRM-IC. The classifiers with the novel distance were implemented in four land cover images. The classification results of the classifier based on our spectral-spatial distance outperformed all the other competitive contextual classifiers, which demonstrated the validity of the proposed distance measure method.
关键词： contextual classifier,remote sensing image classification,information content,statistical region merging
更新于2025-09-23 15:21:21
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Delineation and mapping of coal mine fire using remote sensing data – a review
摘要： Various countries around the globe face numerous hazards due to the burning of coal on the surface as well as below ground. Countries like China, India, United States of America (USA), Australia, Indonesia, and many other countries have reported the burning of coal fires, and thus it is the urgent need to control the coal fire propagation to prevent the loss of energy resources. Coal is a fossil fuel that has a tendency to catch fire for many reasons; spontaneous combustion being the most frequent reasons for its burning. Other factors leading to coal fire include forest fires close to coal seams, natural hazards, old mining techniques, and external heat sources. The review work demonstrates the application of various satellite data in fire detection and mapping. The literature reveals that remote sensing plays an important role in facilitating quick and complete delineation of coal mine fires. Many algorithms have been developed around the world for fire detection from different satellite data. A comprehensive demonstration of different algorithms along with their merits and demerits are outlined. Comparative performances of the different algorithms with their case studies are also explained. It can be inferred from the various literature that it is very difficult to select a particular sensor algorithm for generating global fire products. Suggestions are given to further explore the possibility of improvement of fire detection algorithms.
关键词： remote sensing,fire detection algorithms,coal mine fire,satellite data,thermal anomaly
更新于2025-09-23 15:21:21
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Using Combined Close-Range Active and Passive- Remote Sensing Methods to Detect Sinkholes
摘要： In the Dead Sea region of Israel, sinkholes collapse can be observed easily due to the large number of sites. The continuous decrease in Dead Sea level over the last 30 years, caused a substantial increases the sinkhole activity (more than 5,500 sinkholes upper layer collapse). Sinkholes of up to 50 m diameter are found to be clustered in sites with variable characteristics. In this research, we have developed methods for prediction of sinkholes appearance by using mapping and monitoring methods based on active and passive remote-sensing means. These methods are based on measurements from several instruments including field spectrometry, geophysical ground-penetration radar (GPR) and a frequency domain electromagnetic (FDEM) instrument. Field spectrometry was used to compare the spectral signatures of soil samples collected near progressing sinkholes and those taken in regions with no visible occurrence of sinkholes. Active remote sensing showed higher electrical conductivity and soil moisture in the former regions. Measurements were taken at different time points to monitor the progress of an "embryonic" sinkhole. The research steps included (i) review of previous published literature, (ii) mapping of regions with an abundance of sinkholes in various stages, and areas that are vulnerable to them, (iii) data analysis and development of warning indicators, accessible information to the scientific community. The result derived from this research indicates the possibility to build a pre-warning tool to detect the formation of sinkholes.
关键词： Active remote sensing,Spectroscopy,Ground-penetration radar,Sinkhole,Passive remote sensing,Frequency domain electromagnetic
更新于2025-09-23 15:21:01
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Miniaturised substrate integrated waveguide cavities in dual-band filter and diplexer design
摘要： The Brazilian Legal Amazon (BLA), the largest global rainforest on earth, contains nearly 30% of the rainforest on earth. Given the regional complexity and dynamics, there are large government investments focused on controlling and preventing deforestation. The National Institute for Space Research (INPE) is currently developing five complementary BLA monitoring systems, among which the near real-time deforestation detection system (DETER) excels. DETER employs MODIS 250 m imagery and almost daily revisit, enabling an early warning system to support surveillance and control of deforestation. The aim of this paper is to present the methodology and results of the DETER based on AWIFS data, called DETER-B. Supported by 56 m images, the new system is effective in detecting deforestation smaller than 25 ha, concentrating 80% of its total detections and 45% of the total mapped area in this range. It also presents higher detection capability in identifying areas between 25 and 100 ha. The area estimation per municipality is statistically equal to those of the official deforestation data (PRODES) and allows the identification of degradation and logging patterns not observed with the traditional DETER system.
关键词： remote sensing,rainforest,Monitoring,public policies
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Conference on Information and Communication Technology (CICT) - Allahabad, India (2019.12.6-2019.12.8)] 2019 IEEE Conference on Information and Communication Technology - LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture
摘要： Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identi?cation from the challenging images taken from a height.
关键词： Computer Vision,LED Pattern Identi?cation,UAVs,Wireless Sensor Networks,Precision Agriculture,Remote Sensing
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Determination of Maximum Power Point with a Module to Module Monitoring System, M3S
摘要： We developed and successfully applied data-driven models that heavily rely on readily available remote sensing datasets to investigate probabilities of algal bloom occurrences in Kuwait Bay. An artificial neural network (ANN) model, a multivariate regression (MR) model, and a spatiotemporal hybrid model were constructed, optimized, and validated. Temporal and spatial submodels were coupled in a hybrid modeling framework to improve on the predictive powers of conventional ANN and MR generic models. Sixteen variables (sea surface temperature [SST], chlorophyll a OC3M, chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel-Maritorena (GSM), precipitation, CDOM, turbidity index, PAR, euphotic depth, Secchi depth, wind direction, wind speed, bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture) were used as inputs for the spatial submodel; all of these, with the exception of bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture were used for the temporal submodel as well. Findings include: 1) the ANN model performance exceeded that of the MR model and 2) the hybrid models improved the model performance significantly; 3) the temporal variables most indicative of the timing of bloom propagation are sea surface temperature, Secchi disk depth, wind direction, chlorophyll a (OC3M), and wind speed; and 4) the spatial variables most indicative of algal bloom distribution are the ocean chlorophyll from OC3M, GSM, and the GIOP products; distance to shore; and SST. The adopted methodologies are reliable, cost-effective and could be used to forecast algal bloom occurrences in data-scarce regions.
关键词： remote sensing,Coupled spatiotemporal algal bloom model,data mining,Kuwait bay,neural networks
更新于2025-09-23 15:21:01
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Light Bullets in Passively Mode-Locked Lasers: Dynamics and Instabilities
摘要： Vertically and horizontally inhomogeneous distributions of hydrometeors are often observed in precipitating clouds. The 3-D characteristics can then cause errors in the passive microwave rainfall measurements with the current off-nadir viewing sensors’ specific specifications. This result is due to the fact that the same surface rainfall could be associated with different amounts of hydrometeors depending on the sensors’ viewing paths. In this paper, we confirmed that the plane-parallel radiative treatment to the atmosphere leaves a notable deficiency in the microwave radiometric signatures, particularly at the higher frequency channels for different viewing directions when largely inhomogeneous precipitating clouds are accompanied by significant ice particles. The mean differences between the two brightness temperature fields with two opposite azimuthal viewing directions were up to approximately 40 ?K for the vertically polarized channel at 85.5 GHz in the case study. The impact of the 3-D effect on the passive microwave rainfall estimations was also examined by synthetic retrievals employing a Bayesian methodology. The results showed that the uncertainty in the rainfall estimations due to the 3-D effect depended on the viewing directions considered in the a priori information. It was also found that taking more viewing angles or the azimuth angles in the a priori information into consideration tended to moderate the retrieval difference that resulted from the different viewing directions. In addition, the retrieval uncertainty related to the 3-D effect appeared to be more significant for heavy rainfall cases with large amounts of ice particles, as expected.
关键词： 3-D radiative transfer,precipitation,3-D effect,Passive microwave remote sensing
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Measurement Uncertainty as a Test Model Assessment Tool
摘要： We developed and successfully applied data-driven models that heavily rely on readily available remote sensing datasets to investigate probabilities of algal bloom occurrences in Kuwait Bay. An artificial neural network (ANN) model, a multivariate regression (MR) model, and a spatiotemporal hybrid model were constructed, optimized, and validated. Temporal and spatial submodels were coupled in a hybrid modeling framework to improve on the predictive powers of conventional ANN and MR generic models. Sixteen variables (sea surface temperature [SST], chlorophyll a OC3M, chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel-Maritorena (GSM), precipitation, CDOM, turbidity index, PAR, euphotic depth, Secchi depth, wind direction, wind speed, bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture) were used as inputs for the spatial submodel; all of these, with the exception of bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture were used for the temporal submodel as well. Findings include: 1) the ANN model performance exceeded that of the MR model and 2) the hybrid models improved the model performance significantly; 3) the temporal variables most indicative of the timing of bloom propagation are sea surface temperature, Secchi disk depth, wind direction, chlorophyll a (OC3M), and wind speed; and 4) the spatial variables most indicative of algal bloom distribution are the ocean chlorophyll from OC3M, GSM, and the GIOP products; distance to shore; and SST. The adopted methodologies are reliable, cost-effective and could be used to forecast algal bloom occurrences in data-scarce regions.
关键词： remote sensing,Coupled spatiotemporal algal bloom model,data mining,Kuwait bay,neural networks
更新于2025-09-23 15:21:01
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A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder
摘要： Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches.
关键词： Remote sensing,image classification,Extended Multi-Attribute Profiles,spectral-spatial feature extraction,Sparse Autoencoder,Support Vector Machine
更新于2025-09-23 15:21:01