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oe1(光电查) - 科学论文

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出版时间
  • 2018
研究主题
  • green tide
  • Elegant End-to-End Fully Convolutional Network (E3FCN)
  • deep learning
  • remote sensing
  • Moderate Resolution Imaging Spectroradiometer (MODIS)
应用领域
  • Optoelectronic Information Science and Engineering
机构单位
  • Ocean University of China
485 条数据
?? 中文（中国） ▾

  • A High-Resolution 220-GHz Ultra-Wideband Fully Integrated ISAR Imaging System

    摘要： In this paper, an ultra-wideband fully integrated imaging radar at sub-terahertz (sub-THz) frequencies is presented, which demonstrates a fine lateral resolution without using any focal lens/mirror. We have achieved a lateral resolution of 2 mm for an object at 23-cm distance as well as a range resolution of 2.7 mm. To achieve the decent range resolution, in a frequency modulation continuous wave radar configuration, a state-of-the-art chirp bandwidth (BW) of 62.4 GHz at a center frequency of 221.1 GHz is generated and efficiently radiated. We have presented a design technique for the optimal design of the passive embedding around the core transistor to maximize the tuning BW of the voltage controlled oscillator. At the receiver side, to maximize the intermediate frequency level, a subharmonic mixer is utilized, which is designed for the lowest conversion loss. Finally, to obtain the fine lateral resolution, we have implemented near-field beamforming algorithm based on the inverse synthetic aperture radar (ISAR) systems. The synthesized beamwidth is less than 0.5°; hence, high-resolution images are reconstructed. The system is fabricated in a 55-nm BiCMOS process. To the best of our knowledge, this is the first imaging radar at THz/sub-THz frequencies, which utilizes ISAR to achieve a high lateral resolution while the radar system is fully integrated.

    关键词： ultra-wideband,Frequency modulation continuous wave (FMCW),remote sensing,THz,radar,sub-terahertz (sub-THz),oscillator,inverse synthetic aperture radar (ISAR),plane wave

    更新于2025-09-23 15:21:21

  • Distance-Resolving Raman Radar Based on a Time-Correlated CMOS Single-Photon Avalanche Diode Line Sensor

    摘要： Remote Raman spectroscopy is widely used to detect minerals, explosives and air pollution, for example. One of its main problems, however, is background radiation that is caused by ambient light and sample fluorescence. We present here, to the best of our knowledge, the first time a distance-resolving Raman radar device that is based on an adjustable, time-correlated complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode line sensor which can measure the location of the target sample simultaneously with the normal stand-off spectrometer operation and suppress the background radiation dramatically by means of sub-nanosecond time gating. A distance resolution of 3.75 cm could be verified simultaneously during normal spectrometer operation and Raman spectra of titanium dioxide were distinguished by this system at distances of 250 cm and 100 cm with illumination intensities of the background of 250 lux and 7600 lux, respectively. In addition, the major Raman peaks of olive oil, which has a fluorescence-to-Raman signal ratio of 33 and a fluorescence lifetime of 2.5 ns, were distinguished at a distance of 30 cm with a 250 lux background illumination intensity. We believe that this kind of time-correlated CMOS single-photon avalanche diode sensor could pave the way for new compact distance-resolving Raman radars for application where distance information within a range of several metres is needed at the same time as a Raman spectrum.

    关键词： time-correlated single photon counting (TCSPC),remote Raman spectroscopy,CMOS single-photon avalanche diode (SPAD),time interval measurement,distance-resolving Raman radar,stand-off Raman spectrometer

    更新于2025-09-23 15:21:21

  • [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 - Cnn Based Renormalization Method for Ship Detection in Vhr Remote Sensing Images

    摘要： Ship detection with very high resolution (VHR) remote sensing image has recently been an attractive topic due to rapid development of deep learning. Current researches on ship detection are generally confronted with a big challenge that existing methods failed to get high quality of object proposal with good intersection-over-union (IOU) before detection. In this paper, a Convolutional Neural Network (CNN) based renormalization method is proposed to improve the quality of object proposal. First, CNN is used to predict shape information of candidate ships’ which are involved with rotation, location and scale in patches. Then, a renormalization net is designed to adjust the candidate ships in patches by correcting the shape information and renormalizing it to uniform patch. In this way, good candidate objects in patches could be generated and will be helpful with improving following ship detection. The proposed renormalization net was tested on a Google-Earth handcraft dataset. The experimental result demonstrates the proposed renormalization net greatly improve the ship detection with both of good detection accuracy and high IOU.

    关键词： Ship detection,CNN,renormalization,remote sensing

    更新于2025-09-23 15:21:21

  • Uncertainty budgets of major ozone absorption cross sections used in UV remote sensing applications

    摘要： Detailed uncertainty budgets of three major ultraviolet (UV) ozone absorption cross-section datasets that are used in remote sensing application are provided and discussed. The datasets are Bass–Paur (BP), Brion–Daumont–Malicet (BDM), and the more recent Serdyuchenko–Gorshelev (SG). For most remote sensing application the temperature dependence of the Huggins ozone band is described by a quadratic polynomial in temperature (Bass–Paur parameterization) by applying a regression to the cross-section data measured at selected atmospherically relevant temperatures. For traceability of atmospheric ozone measurements, uncertainties from the laboratory measurements as well as from the temperature parameterization of the ozone cross-section data are needed as input for detailed uncertainty calculation of atmospheric ozone measurements. In this paper the uncertainty budgets of the three major ozone cross-section datasets are summarized from the original literature. The quadratic temperature dependence of the cross-section datasets is investigated. Combined uncertainty budgets is provided for all datasets based upon Monte Carlo simulation that includes uncertainties from the laboratory measurements as well as uncertainties from the temperature parameterization. Between 300 and 330 nm both BDM and SG have an overall uncertainty of 1.5 %, while BP has a somewhat larger uncertainty of 2.1 %. At temperatures below about 215 K, uncertainties in the BDM data increase more strongly than the others due to the lack of very low temperature laboratory measurements (lowest temperature of BDM available is 218 K).

    关键词： uncertainty budgets,Monte Carlo simulation,temperature dependence,UV remote sensing,ozone absorption cross sections

    更新于2025-09-23 15:21:21

  • Effect of Open Soil Surface Patterns on Soil Detectability Based on Optical Remote Sensing Data

    摘要： Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R2cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22–46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results.

    关键词： digital soil mapping,remote sensing,spectral reflectance,surface feedback

    更新于2025-09-23 15:21:21

  • [IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Oil-Palm Tree Detection in Aerial Images Combining Deep Learning Classifiers

    摘要： Palm oil is the largest vegetable oil in the world in terms of produced volume, and 75% of global production is used for food and cooking purposes. Sustainable management of the producing areas calls for the frequent assessment of field conditions. In this paper, we investigate an automatic algorithm based on deep learning that is capable to build an inventory of individual oil-palm trees using aerial color images collected by unmanned aerial vehicles. The idea consists of combining the outputs of two independent convolutional neural networks, trained on partially distinct subsets of samples and different spatial scales to capture coarse and fine details of image patches. The estimated posterior probabilities are combined by simple averaging as to improve detection accuracy and estimate the confidence for each individual detection. Non-maxima suppression removes weak detections. Experiments at three commercial oil-palm tree plantations sites aged two, four, and 16 years in Northern Brazil revealed overall detection accuracies in the range 91.2–98.8% using orthomosaics of decimeter spatial resolution. The proposed approach can be a useful component of a forest monitoring system based on remote sensing.

    关键词： convolutional neural networks,classification,Tree counting,remote sensing,forest inventory

    更新于2025-09-23 15:21:21

  • [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 Semantic Hashing Retrieval of Remotec Sensing Images

    摘要： Due to the rapid evolution of satellite systems, traditional nearest neighbor image retrieval methods used in large-scale image retrieval usually cause "curse of dimensionality" that leads to boosting feature storage and slow retrieval speed. The hashing method, which aims at mapping the high-dimensional data to compact binary hash codes in Hamming space and quickly calculates the Hamming distance by bit operation and XOR operation, can effectively achieve search and retrieval with remaining similarity for big data. In this paper, we propose a novel image retrieval method based on deep hashing learning, called deep semantic hashing(DSH), attempting to mining the semantic information of remote sensing(RS) images. Experiments carried out on an archive of RS images point out that DSH outperforms other methods to achieve the state-of-the-art performance in image retrieval applications.

    关键词： image retrieval,semantic mining,Remote sensing,deep learning,hashing methods

    更新于2025-09-23 15:21:21

  • [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 - Effects of Nonuniform Vertical Profiles of Suspended Particles on Remote Sensing Reflectance of Turbid Water

    摘要： The in situ data and forward radiative transfer model were applied to simulate the nonuniform vertical profiles of suspended particles in turbid Poyang Lake. The sensitivity of remote sensing reflectance (Rrs) associated with nonuniform water column showed correlation with suspended particulate matter (SPM), wavelength and water depth. Different nonuniform vertical profiles could cause more than 108% overestimation or 60% underestimation of Rrs at most. The uncertainties in Rrs decreased with the increase of water depth. The sensitive wavelength moved to longer wavelength and the maximum influence water depth let up, along with the increase in concentration of SPM in surface water. A dimensionless parameter made up of beam attenuation coefficient of surface water, water depth and SPM vertical distribution, was established to quantitatively describe the effects of vertically nonuniform water column on Rrs.

    关键词： vertically nonuniform water column,water optical properties,remote sensing reflectance

    更新于2025-09-23 15:21:21

  • [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 - Automated Analysis of Remotely Sensed Images Using the Unicore Workflow Management System

    摘要： The progress of remote sensing technologies leads to increased supply of high-resolution image data. However, solutions for processing large volumes of data are lagging behind: desktop computers cannot cope anymore with the requirements of macro-scale remote sensing applications; therefore, parallel methods running in High-Performance Computing (HPC) environments are essential. Managing an HPC processing pipeline is non-trivial for a scientist, especially when the computing environment is heterogeneous and the set of tasks has complex dependencies. This paper proposes an end-to-end scientific workflow approach based on the UNICORE workflow management system for automating the full chain of Support Vector Machine (SVM)-based classification of remotely sensed images. The high-level nature of UNICORE workflows allows to deal with heterogeneity of HPC computing environments and offers powerful workflow operations such as needed for parameter sweeps. As a result, the remote sensing workflow of SVM-based classification becomes re-usable across different computing environments, thus increasing usability and reducing efforts for a scientist.

    关键词： High-Performance Computing (HPC),Remote Sensing,Scientific Workflows,UNICORE,Support Vector Machine (SVM)

    更新于2025-09-23 15:21:21

  • Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS

    摘要： As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.

    关键词： remote sensing,plant breeding,crop,proximal sensing,phenotyping

    更新于2025-09-23 15:21:21