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Erratum to:Decadal glacial lake changes in the Koshi basin,central Himalaya,from 1977 to 2010,derived from Landsat satellite images

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Journal of Mountain Science 2022年第8期19卷 2449-2450页

作者： Finu SHRESTHA GAO Xiao Narendra Raj KHANAL Sudan Bikash MAHARJAN Rajendra Bahadur SHRESTHA WU Li-zong Pradeep Kumar MOOL Samjwal Ratna BAJRACHARYA International Centre for Integrated Mountain Development(ICIMOD) 44600 KathmanduNepal Cold and Arid Regions Environmental and Engineering Research Institute(CAREERI) CASLanzhou 730000China Central Department of Geography Tribhuvan University44600 KirtipurNepal Polar Research Institute of China Shanghai 200135China

The article Decadal glacial lake changes in the Koshi basin,central Himalaya,from 1977 to 2010,derived from Landsat satellite images,written by Finu SHRESTHA,GAO Xiao,Narendra Raj KHANAL,Sudan Bikash MAHARJAN,Rajendra Bahadur SHRESTHA,WU Li-zong,Pradeep Kumar MOOL and Samjwal Ratna BAJRACHARYA,was originally erroneously published electronicaliy on the publisher's internet portal(currently SpringerLink)on 04 October 2017 without open access.

关键词： glacial basin images

images of triple gas electron multiplier with pixel-pads

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Chinese Physics B 2009年第10期18卷 4229-4233页

作者：董静胡碧涛陈元柏谢一冈 College of Nuclear Science and Technology Lanzhou University Institute of High Energy Physics Chinese Academy of Sciences

Testing of a triple gas electron multiplier （GEM） with pixel-pads is described. images by scanning and suspending radioactive sources were obtained by using 96 channels digital data acquisition （DAQ） system which ... 详细信息

关键词： gas electron multiplier images digital data acquisition system

A novel visual attention method for target detection from SAR images

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Chinese Journal of Aeronautics 2019年第8期32卷 1946-1958页

作者： Fei GAO Aidong LIU Kai LIU Erfu YANG Amir HUSSAIN School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Space Mechatronic Systems Technology Laboratory Department of DesignManufacture and Engineering ManagementUniversity of StrathclydeGlasgow G1 1XJUK Cognitive Big Data and Cyber-Informatics(CogBID)Laboratory School of ComputingEdinburgh Napier UniversityEdinburgh EH10 5DTScotlandUK

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

关键词： Learning strategy Synthetic Aperture Radar(SAR) images Target detection Top-down Visual attention mechanism

Crowd counting via learning perspective for multi-scale multi-view Web images

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Frontiers of Computer Science 2019年第3期13卷 579-587页

作者： Chong SHANG Haizhou AI Yi YANG Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China Huawei Technologies Beijing 100084 China

Estimating the number of people in Web images still remains a challenging problem owing to the perspective variation, different views, and diverse backgrounds. Existing deep learning models still have difficulties in dealing with scenarios where the size of a person is either extremely large or extremely small. In this paper, we propose a novel perspective-aware architecture to estimate the number of people in a crowd in web images. Specifically, we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to regress the final count. Experiments on challenging datasets demonstrate our approach can deal with a large perspective variation and that we have achieved state-of-theart results.

关键词： crowd counting Web images perspective inference

Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

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Frontiers of Earth Science 2019年第3期13卷 495-509页

作者： Zhuokun PAN Yueming HU Guangxing WANG College of Natural Resources and Environment South China Agricultural UniversityGuangzhou 510642China Key Laboratory of Construction Land Transformation Ministry of Land and ResourcesGuangzhou 510642China Guangdong Provincial Key Laboratory of Land Use and Consolidation Guangzhou 510642China Guangdong Provincial Land Information Engineering Research Center Guangzhou 510642China College of Agriculture and Animal Husbandry Qinghai UniversityXining 810016China Department of Geography Southern Illinois University at CarbondaleIL 62901USA

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

关键词： Sentinel-1 SAR time series images urban land use change detection temporal endmember spectral angle mapping

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

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International Journal of Ophthalmology(English edition) 2019年第6期12卷 1012-1020页

作者： Meng-Xiao Li Su-Qin Yu Wei Zhang Hao Zhou Xun Xu Tian-Wei Qian Yong-Jing Wan School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Department of Ophthalmology Shanghai General HospitalShanghai Jiaotong University School of MedicineShanghai Key Laboratory of Ocular Fundus DiseasesShanghai 200080China

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

关键词： optical coherence tomography images fluid segmentation 2D fully convolutional network 3D fully convolutional network

Assessment of desertification in Eritrea: land degradation based on Landsat images

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Journal of Arid Land 2019年第3期11卷 319-331页

作者： Mihretab G GHEBREZGABHER YANG Taibao YANG Xuemei WANG Congqiang Institute of Glaciology and Eco-Geography College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China College of Business and Social Sciences Adi Keihi 59Eritrea Gansu Desert Control Research Institute Lanzhou 730070China

Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions.In this study,we used a decision tree method based on NDVI(normalized difference vegetation index),SAVI(soil adjusted vegetation index),and vegetation cover proportion to quantify and analyze the desertification in Eritrea using Landsat data of the 1970 s,1980 s and 2014.The results demonstrate that the NDVI value and the annual mean precipitation declined while the temperature increased over the past 40 a.Strongly desertified land increased from 4.82×10^4 km^2(38.5%)in the 1970 s to 8.38×10^4 km^2(66.9%)in 2014:approximately 85%of the land of the country was under serious desertification,which significantly occurred in arid and semi-arid lowlands of the country(eastern,northern,and western lowlands)with relatively scarce precipitation and high temperature.The non-desertified area,mostly located in the sub-humid eastern escarpment,also declined from approximately 2.1%to 0.5%.The study concludes that the desertification is a cause of serious land degradation in Eritrea and may link to climate changes,such as low and unpredictable precipitation,and prolonged drought.

关键词： desertification Landsat images NDVI index SPI analysis Eritrea

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

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Journal of Systems Engineering and Electronics 2019年第2期30卷 238-244页

作者： LI Binquan HU Xiaohui School of Automation Science and Electrical Engineering Beihang University Institute of Software Chinese Academy of Sciences

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

关键词： convolutional neural network (CNN) distributed architecture remote sensing images (RSIs) target classification pre-training

Early esophagus cancer segmentation from gastrointestinal endoscopic images based on U-Net++model

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电子科技学刊:英文版 2023年第3期21卷 38-51页

作者： Zenebe Markos Lonseko Cheng-Si Luo Wen-Ju Du Tao Gan Lin-Lin Zhu Prince Ebenezer Adjei Ni-Ni Rao School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu610054China College of Health and Medical Sciences Dilla UniversityDilla419Ethiopia West China Hospital of Sichuan University Chengdu610017China

Automatic segmentation of early esophagus cancer(EEC)in gastrointestinal endoscopy(GIE)images is a critical and challenging task in clinical settings,which relies primarily on labor-intensive and time-consuming routines.EEC has often been diagnosed at the late stage since early signs of cancer are not obvious,resulting in low survival rates.This work proposes a deep learning approach based on the U-Net++method to segment EEC in GIE images.A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center,West China Hospital of Sichuan University,China,have been utilized.The experimental result shows that our proposed method achieved promising results.Furthermore,the comparison has been made between the proposed and other U-Net-related methods using the same dataset.The mean and standard deviation(SD)of the dice similarity coefficient(DSC),intersection over union(IoU),precision(Pre),and recall(Rec)achieved by the proposed framework were DSC(%)=94.62±0.02,IoU(%)=90.99±0.04,Pre(%)=94.61±0.04,and Rec(%)=95.00±0.02,respectively,outperforming the others.The proposed method has the potential to be applied in EEC automatic diagnoses.

关键词： Early esophageal cancer(EEC) Gastrointestinal endoscopic(GIE) images Semantic segmentation Supervised learning U-Net++