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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） ▾

  • Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery

    摘要： Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.

    关键词： dense semantic labeling,encoder-decoder,superpixel-based DenseCRF,remote sensing imagery,fully convolutional networks,atrous spatial pyramid pooling

    更新于2025-09-23 15:23:52

  • Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients

    摘要： The Markov random ?eld (MRF) model is a widely used method for remote-sensing image segmentation, especially the object-based MRF (OMRF) method has attracted great attention in recent years. However, the OMRF method usually fails to capture the correlation between regional features by just considering the mixed-Gaussian model. In order to solve this problem and improve the segmentation accuracy, this article proposes a new method, object-based Gaussian-Markov random ?eld model with region coe?cients (OGMRF-RC), for remote-sensing image segmentation. First, to describe the complicated interactions among regional features, the OGMRF-RC method employs the region size and edge information as region coe?cients to build the each object-based region. Second, the classic Gaussian-Markov model is extended to region level for modelling the errors in OLREs. Finally, the segmentation is achieved through a principled probabilistic inference designed for the OGMRF-RC method. Experimental results over synthetic texture images and remote-sensing images from di?erent datasets show that the proposed OGMRF-RC method can achieve more accurate segmentation than other state-of-the-art MRF-based methods and the method using convolutional neural networks.

    关键词： Segmentation,Gaussian-Markov random field,region coefficients,object-based,remote-sensing imagery

    更新于2025-09-23 15:23:52

  • Rapid Multisite Remote Surface Dosimetry for Total Skin Electron Therapy: Scintillator Target Imaging

    摘要： Verifying radiation-field uniformity in total skin electron therapy is important to ensuring adequate and effective treatment administration. This clinical study presents a novel, scintillation-based, optical-imaging technique for conducting surface dosimetry in patients undergoing total skin electron therapy. The system exceeded the ease of use of established dosimetry techniques at a similar level of accuracy.

    关键词： total skin electron therapy,optical imaging,surface dosimetry,scintillator,remote dosimetry

    更新于2025-09-23 15:23:52

  • Cloud removal in remote sensing images using nonnegative matrix factorization and error correction

    摘要： In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier to the effective observation of sensors. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix factorization (NMF) and error correction method (S-NMF-EC) is proposed in this paper. Firstly, a cloud-free fused reference image is obtained by a reference image and two or more low-resolution images using the spatial and temporal nonlocal filter-based data fusion model (STNLFFM). Secondly, the cloud-free fused reference image is used to remove the cloud cover of the cloud-contaminated image based on NMF. Finally, the cloud removal result is further improved by error correction. It is worth noting that cloud detection is not required by S-NMF-EC, and the cloud-free information of the cloud-contaminated image is maximally retained. Both simulated and real-data experiments were conducted to validate the proposed S-NMF-EC method. Compared with other cloud removal methods, the results demonstrate that S-NMF-EC is visually and quantitatively effective (correlation coefficients ≥ 0.99) for the removal of thick clouds, thin clouds, and shadows.

    关键词： Nonnegative matrix factorization,Multitemporal,Optical remote sensing image,Error correction,Cloud removal

    更新于2025-09-23 15:23:52

  • Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images

    摘要： Cultivation of olive trees for the past few years has been widely spread across Mediterranean countries, including Spain, Greece, Italy, France, and Turkey. Among these countries, Spain is listed as the largest olive producing country with almost 45% of olive oil production per year. Dedicating land of over 2.4 million hectares for the olive cultivation, Spain is among the leading distributors of olives throughout the world. Due to its high signi?cance in the country’s economy, the crop yield must be recorded. Manual collection of data over such expanded ?elds is humanly infeasible. Remote collection of such information can be made possible through the utilization of satellite imagery. This paper presents an automated olive tree counting method based on image processing of satellite imagery. The images are pre-processed using the unsharp masking followed by improved multi-level thresholding-based segmentation. Resulting circular blobs are detected through the circular Hough transform for identi?cation. Validation has been performed by evaluating the proposed scheme for the dataset formed by acquiring images through the ‘‘El Sistema de Información Geográ?ca de Parcelas Agrícolas’’ viewer over the region of Spain. The proposed algorithm achieves an accuracy of 96% in detection. Computation time was recorded as 24 ms for an image size of 300 × 300 pixels. The less spectral information is used in our proposed methodology resulting in a competitive accuracy with low computational cost in comparison to the state-of-the-art technique.

    关键词： crop estimation,multi-spectral imagery,Remote sensing,olive,Hough transform,satellite imagery

    更新于2025-09-23 15:23:52

  • [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 - A Novel Effective Chlorophyll Indicator for Forest Monitoring Using Worldview-3 Multispectral Reflectance

    摘要： This paper explores the feasibility of deriving multispectral-based effective chlorophyll indicators (MECIs) for foliage chlorophyll concentration (CHLS) estimation. An average fusion method was applied to simulate the multispectral reflectance of the WorldView-3 sensor using hyperspectral data. With the experimental data of CHLS and predictors derived from multispectral reflectance, a series of linear regression analyses were carried out to derive appropriate models for CHLS estimation. Accuracy measures of RMSE and PRMSE were used to evaluate the model performance. Results showed that the coastal-band based MECI (MECIc) and the blue-band based MECI (MECIb) were able to achieve an RMSE of 0.5657 mg/g and 0.5943 mg/g as well as a PRMSE of 36% and 38% respectively. Using the Red edge and Yellow reflectance based NDVI (NDVIREY) as a predictor, the model can reduce uncertainty and achieve an estimation of 0.4089 mg/g and 26% for RMSE and PRMSE respectively. The prediction error made by the CHLS-NDVIREY model and the CHLS-MECI model were 11% and 60% larger than 0.38 mg/g the RMSE of hyperspectral-based CHLS-ECI model. In summary, NDVIREY was able to achieve a better prediction at around a level of 75% accuracy (1-PRMSE) and therefore is able to be an effective indicator of CHLS for forest monitoring.

    关键词： climate change,hyperspectral remote sensing,Chlorophyll indicator,multispectral remote sensing,forest health

    更新于2025-09-23 15:22:29

  • [IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Human-Machine Interface for Remote Training of Robot Tasks.

    摘要： Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the “remote” robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.

    关键词： Human-Machine Interface,Robot Tasks,Hand Detection,Remote Robots,Remote Training,Dynamic Motion Primitives

    更新于2025-09-23 15:22:29

  • [IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN

    摘要： Aiming at the problem of traditional low-level recognition of key targets in remote sensing images, a method for target detection and recognition based on Faster R-CNN is proposed. Firstly, the open source remote sensing image data set NWPU VHR-10 dataset is converted into VOC 2007 format as the training sets and test sets. Secondly, according to the training set category information, the hyper-parameters of the neural network are refined, and then the training set is trained using the Faster R-CNN neural network to generate a model. Finally, this model is used to detect unknown remote sensing images and identify important targets. The simulation results show that the method has high recognition accuracy and speed, and can provide reference for recognition of the key targets of remote sensing images.

    关键词： Faster R-CNN,convolution neural network,deep learning,key target recognition,remote sensing image detection

    更新于2025-09-23 15:22:29

  • [IEEE 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC) - Mon Tresor, Plaine Magnien, Mauritius (2018.12.6-2018.12.7)] 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC) - Development of an IR-based Device for Wireless Communication in Community Health Centres

    摘要： With the increase in implementation of advanced communication equipment, especially in the medical sector, finding solutions to anticipated problems has become paramount. Our envisaged problem is the uneasy procedure of taking patients’ variables frequently in community health centers, where there is usually minimal support staff. This paper indicates that human variables such as body temperature and movement at a special ward in a community health care center are transferable from one computer to another. However, our implementation integrated a temperature sensor (LM35), pyroelectric infrared, and infrared to a low-cost Arduino-generic Lilypad microcontroller (as the transmitter). The developed device was able to communicate with the receiver side (comprised of Arduino Mega, infrared receiver, and a computer). We were able to transfer data seamlessly from Mr. C. E. Ngene to a remote computer for analysis. This project will make up for the unavailability of expensive devices and low workforce in the rural community health centers. This designed device can help patients to be attended to quickly as the patients start to have issues.

    关键词： sensors,infrared,health,remote monitoring,optical wireless communication

    更新于2025-09-23 15:22:29

  • [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 - Accurate Building Detection in VHR Remote Sensing Images Using Geometric Saliency

    摘要： This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR). Inspired by the observation that buildings are always more distinguishable in geometries than in texture or spectral, we propose a new geometric building index (GBI) for accurate building detection, which relies on the geometric saliency of building structures. The geometric saliency of buildings is derived from a mid-level geometric representation based on meaningful junctions that can locally describe anisotropic geometrical structures of images. The resulting GBI is measured by integrating the derived geometric saliency of buildings. Experiments on three public datasets demonstrate that the proposed GBI achieves very promising performance, and meanwhile shows impressive generalization capability.

    关键词： remote sensing image,geometric saliency,junction,Building detection

    更新于2025-09-23 15:22:29