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Evaluation of Landsat-8 TIRS data recalibrations and land surface temperature split-window algorithms over a homogeneous crop area with different phenological land covers ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-03-04 Raquel Niclòs; Jesús Puchades; César Coll; María J. Barberà; Lluís Pérez-Planells; José A. Valiente; Juan M. Sánchez
Successive re-calibrations were implemented in Landsat-8 TIRS data since launch. This paper evaluates the performances of both: (1) these re-calibrations, up to the last calibration update announced for TIRS data in the next Landsat Collection 2; and (2) single-channel (SC) corrections and split-window (SW) algorithms to retrieve land surface temperature (LST) from TIRS data. A robust and accurate
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Deep multisensor learning for missing-modality all-weather mapping ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-03-04 Zhuo Zheng; Ailong Ma; Liangpei Zhang; Yanfei Zhong
Multisensor Earth observation has significantly accelerated the development of multisensor collaborative remote sensing applications such as all-weather mapping using synthetic aperture radar (SAR) images and optical images. However, in the real-world application scenarios, not all data sources may be available, namely, the missing-modality problem, e.g., the poor imaging conditions obstruct the optical
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Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-27 Matheus Henrique Tavares; Regina Camara Lins; Tristan Harmel; Carlos Ruberto Fragoso Jr.; Jean-Michel Martínez; David Motta-Marques
Remote monitoring of chlorophyll-a (chla) has been widely used to evaluate the trophic state of inland and coastal waters, however, there is still much uncertainty in the algorithms applied in different optical water types. The influence of different atmospheric correction (AC) processors, which can also provide correction for sunglint and adjacency effects, on the retrieved chla is poorly understood
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VPC-Net: Completion of 3D vehicles from MLS point clouds ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-26 Yan Xia; Yusheng Xu; Cheng Wang; Uwe Stilla
As a dynamic and essential component in the road environment of urban scenarios, vehicles are the most popular investigation targets. To monitor their behavior and extract their geometric characteristics, an accurate and instant measurement of vehicles plays a vital role in traffic and transportation fields. Point clouds acquired from the mobile laser scanning (MLS) system deliver 3D information of
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A maximum bathymetric depth model to simulate satellite photon-counting lidar performance ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-26 Wenhao Zhang; Nan Xu; Yue Ma; Bisheng Yang; Zhiyu Zhang; Xiao Hua Wang; Song Li
With the development of photon-counting sensors, spaceborne photon-counting lidars have shown many advantages in mapping underwater topography. Although a space based lidar is normally a profiling system, the depth penetration and the vertical accuracy achieved with a bathymetric lidar is superior to imagery (that only provides relative depths). Therefore, many satellite derived bathymetry products
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Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-26 Pengliang Wei; Dengfeng Chai; Tao Lin; Chao Tang; Meiqi Du; Jingfeng Huang
Identifying spatial distribution of crop planting in large-scale is one of the most significant applications of remote sensing imagery. As an active remote sensing system, synthetic aperture radar (SAR) provides high-resolution polarimetric information of land covers. Nowadays, it is possible to carry out continuous multi-temporal analysis of crops in large-scales since an increased number of spaceborne
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Open-air grape classification and its application in parcel-level risk assessment of late frost in the eastern Helan Mountains ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-24 Wei Liu; Xiaodong Zhang; Fei He; Quan Xiong; Xuli Zan; Zhe Liu; Dexuan Sha; Chaowei Yang; Shaoming Li; Yuanyuan Zhao
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A nested drone-satellite approach to monitoring the ecological conditions of wetlands ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-25 Saheba Bhatnagar; Laurence Gill; Shane Regan; Stephen Waldren; Bidisha Ghosh
Monitoring wetlands is necessary in order to understand and protect their ecohydrological balance. In Ireland, traditionally wetland-monitoring is carried out by manual field visits which can be very time-consuming. To automate the process, this study extends the ability of remote sensing-based monitoring of wetlands by combining RGB image processing, machine learning algorithms, and satellite data
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Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-24 Jian Sun; Fangcao Xu; Guido Cervone; Melissa Gervais; Christelle Wauthier; Mark Salvador
Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages
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Depth-enhanced feature pyramid network for occlusion-aware verification of buildings from oblique images ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-23 Qing Zhu; Shengzhi Huang; Han Hu; Haifeng Li; Min Chen; Ruofei Zhong
Detecting the changes of buildings in urban environments is essential. Existing methods that use only nadir images suffer from severe problems of ambiguous features and occlusions between buildings and other regions. Furthermore, buildings in urban environments vary significantly in scale, which leads to performance issues when using single-scale features. To solve these issues, this paper proposes
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A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-22 Xi Wu; Zhenwei Shi; Zhengxia Zou
Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked.
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Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-20 Mingxing Liu; Jianhong Liu; Clement Atzberger; Ya Jiang; Minfei Ma; Xunmei Wang
Zanthoxylum bungeanum Maxim (ZBM) is an important woody species in large parts of Asia, which provides oils and medicinal materials. Timely and accurate mapping of its spatial distribution and planting area is of great significance to local economy and ecology. As a special tree species planted in the Grain for Green Program of China, Linxia Hui Autonomous Prefecture (Linxia) in Gansu Province of China
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Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-19 Chaoyang Niu; Haobo Zhang; Wei Liu; Runsheng Li; Tao Hu
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A hybrid global structure from motion method for synchronously estimating global rotations and global translations ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-18 Xin Wang; Teng Xiao; Yoni Kasten
Over the last few decades, the methods of global image orientation, which is also called global SfM, have attracted a lot of attention from researchers, mainly thanks to its advantage of time efficiency. Based on the input of relative orientation results, most conventional global SfM methods employ a two-step strategy consisting of global rotation estimation and global translation estimation. This
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A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-13 Lucas Prado Osco; Mauro dos Santos de Arruda; Diogo Nunes Gonçalves; Alexandre Dias; Juliana Batistoti; Mauricio de Souza; Felipe David Georges Gomes; Ana Paula Marques Ramos; Lúcio André de Castro Jorge; Veraldo Liesenberg; Jonathan Li; Lingfei Ma; José Marcato; Wesley Nunes Gonçalves
Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering
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Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-14 Amir Hossein Safaie; Heidar Rastiveis; Alireza Shams; Wayne A. Sarasua; Jonathan Li
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M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-09 Lei Deng; Jie Sun; Yong Chen; Han Lu; Fuzhou Duan; Lin Zhu; Tianxing Fan
Hyperspectral remote sensing can get spatially and spectrally continuous data simultaneously. However, the imaging equipment is usually expensive and complex, along with the low spatial resolution. In recent years, reconstruction of hyperspectral image by deep learning from the widely used low-cost, high spatial resolution RGB camera, has attracted extensive attention in many fields. However, most
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Remote sensing image segmentation advances: A meta-analysis ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-08 Ioannis Kotaridis; Maria Lazaridou
The advances in remote sensing sensors during the last two decades have led to the production of very high spatial resolution multispectral images. In order to adapt to this rapid development and handle these data, object-based analysis has emerged. A critical part of such an analysis is image segmentation. The selection of optimal segmentation parameters' values generates a qualitative segmentation
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Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-08 Katharina Anders; Lukas Winiwarter; Hubert Mara; Roderik Lindenbergh; Sander E. Vos; Bernhard Höfle
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Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-06 Martin Danner; Katja Berger; Matthias Wocher; Wolfram Mauser; Tobias Hank
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Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-04 Xuejian Li; Huaqiang Du; Guomo Zhou; Fangjie Mao; Meng Zhang; Ning Han; Weiliang Fan; Hua Liu; ZiHao Huang; Shaobai He; Tingting Mei
Phenology plays an important role in revealing the spatiotemporal evolution of forest ecosystem carbon cycles. The accuracy of vegetation phenology estimates based on remote sensing has improved in temperate zones. However, subtropical vegetation is complex, and the corresponding phenology estimates using remote sensing face great challenges. Bamboo forests are subtropical unique forest types and exhibit
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Semantics-guided reconstruction of indoor navigation elements from 3D colorized points ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-02-02 Juntao Yang; Zhizhong Kang; Liping Zeng; Perpetual Hope Akwensi; Monika Sester
The increasing availability of both indoor positioning services and sensors for 3D data capture, such as RGB-D sensors, allows the provision of indoor spatial information services for indoor localization-based applications. To efficiently realize these services, the indoor information and the relationships between indoor spaces are required. The recently released Indoor Geography Markup Language (IndoorGML)
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UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-30 Sumanta Das; Jack Christopher; Armando Apan; Malini Roy Choudhury; Scott Chapman; Neal W. Menzies; Yash P. Dang
Sodicity is a major soil constraint in many arid and semi-arid regions worldwide, including Australia, which adversely affects the ability of crops to take up water and nutrients from the soil, reducing yield. Reliable methods and tools are required for appropriate selection of traits, may provide a better understanding of crop responses to multiple stresses, especially in sodic soil. A novel strategy
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Monitoring spatiotemporal variation in beach surface moisture using a long-range terrestrial laser scanner ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-25 Junling Jin; Jeffrey Verbeurgt; Lars De Sloover; Cornelis Stal; Greet Deruyter; Anne-Lise Montreuil; Sander Vos; Philippe De Maeyer; Alain De Wulf
The measurement of surface moisture on beaches is vital for studying aeolian sand transport mechanisms, but existing techniques are not adequate for monitoring the surface moisture dynamics over a substantial beach section. In this study, we investigated the suitability of a new remote sensing method to monitor the spatiotemporal variation in surface moisture on a sandy beach using a long-range static
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Efficient global color correction for large-scale multiple-view images in three-dimensional reconstruction ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-26 Junxing Yang; Lulu Liu; Jiabin Xu; Yi Wang; Fei Deng
Consistent global color correction across multiple-view images in three-dimensional (3D) reconstruction is an important and challenging problem. The present work addresses this issue by proposing a novel global color correction method for multi-view images based on a spline curve remapping function. In contrast to existing methods, we obtain a series of optimal functions by minimizing the variance
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Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-23 J.L. Pancorbo; B.T. Lamb; M. Quemada; W.D. Hively; I. Gonzalez-Fernandez; I. Molina
Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different
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Airborne LiDAR point cloud classification with global-local graph attention convolution neural network ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-23 Congcong Wen; Xiang Li; Xiaojing Yao; Ling Peng; Tianhe Chi
Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process
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Automated digital elevation model (DEM) generation from very-high-resolution Planet SkySat triplet stereo and video imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-22 Shashank Bhushan; David Shean; Oleg Alexandrov; Scott Henderson
The Planet SkySat-C SmallSat constellation can acquire very high resolution (0.7 m to 0.9 m) triplet stereo and video imagery with short revisit times, providing an exciting opportunity for global, on-demand 3D mapping of dynamic surface features. However, a lack of suitable processing software, limited geolocation accuracy, and scene-to-scene offsets currently limit the potential for accurate SkySat
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Feature-preserving 3D mesh simplification for urban buildings ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-18 Minglei Li; Liangliang Nan
The goal of urban building mesh simplification is to generate a compact representation of a building from a given mesh. Local smoothness and sharp contours of urban buildings are important features for converting unstructured data into solid models, which should be preserved during the simplification. In this paper, we present a general method to filter and simplify 3D building mesh models, capable
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Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-18 Juepeng Zheng; Haohuan Fu; Weijia Li; Wenzhao Wu; Le Yu; Shuai Yuan; Wai Yuk William Tao; Tan Kian Pang; Kasturi Devi Kanniah
For both the positive economic benefit and the negative ecological impact of the rapid expansion of oil palm plantations in tropical developing countries, it is significant to achieve accurate detection for oil palm trees in large-scale areas. Especially, growing status observation and smart oil palm plantation management enabled by such accurate detections would improve plantation planning, oil palm
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Mapping coastal salt marshes in China using time series of Sentinel-1 SAR ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-18 Yuekai Hu; Bo Tian; Lin Yuan; Xiuzhen Li; Ying Huang; Runhe Shi; Xiaoyi Jiang; lihua Wang; Chao Sun
Salt marshes provide crucial ecological functions and services and are experiencing rapid losses and degradation under global climate changes and high-intensity human activities in coastal zone. However, mapping salt marsh distributions and compositions with high accuracy on the national or global scale remains challenging. Here, we used Sentinel-1 time-series data and knowledge-based automatic decision
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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-16 Qiming Zheng; Qihao Weng; Ke Wang
Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from
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Review on Convolutional Neural Networks (CNN) in vegetation remote sensing ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-16 Teja Kattenborn; Jens Leitloff; Felix Schiefer; Stefan Hinz
Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate
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PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-16 Xian Sun; Peijin Wang; Cheng Wang; Yingfei Liu; Kun Fu
In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in
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Enhanced trajectory estimation of mobile laser scanners using aerial images ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-17 Zille Hussnain; Sander Oude Elberink; George Vosselman
Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with
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Robust unsupervised small area change detection from SAR imagery using deep learning ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-17 Xinzheng Zhang; Hang Su; Ce Zhang; Xiaowei Gu; Xiaoheng Tan; Peter M. Atkinson
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Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-15 Tamer ElGharbawi; Fawzi Zarzoura
Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions
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AMENet: Attentive Maps Encoder Network for trajectory prediction ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-14 Hao Cheng; Wentong Liao; Michael Ying Yang; Bodo Rosenhahn; Monika Sester
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GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-11 Hao Zhang; Jiayi Ma
Pansharpening aims to fuse low-resolution multi-spectral image and high-resolution panchromatic (PAN) image to produce a high-resolution multi-spectral (HRMS) image. In this paper, a new residual learning network based on gradient transformation prior, termed as GTP-PNet, is proposed to generate the high-quality HRMS image with accurate spectral distribution as well as reasonable spatial structure
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Deep regression for LiDAR-based localization in dense urban areas ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-12 Shangshu Yu; Cheng Wang; Zenglei Yu; Xin Li; Ming Cheng; Yu Zang
LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an accurate relative pose). In this work, we develop a novel end-to-end, deep multi-task network that simultaneously
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Combining graph-cut clustering with object-based stem detection for tree segmentation in highly dense airborne lidar point clouds ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-08 Sebastian Dersch; Marco Heurich; Nina Krueger; Peter Krzystek
Single tree detection has been a major research topic when it comes to support of collecting automatic field inventory using lidar. All previous methods show under- and over-segmentation effects because the associated control parameters have a limited scope. This paper describes a novel integrated single tree segmentation using a graph-cut clustering method that is supported by automatic stem detection
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Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-04 I. Toschi; E.M. Farella; M. Welponer; F. Remondino
A big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense
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SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-04 Ailong Ma; Yuting Wan; Yanfei Zhong; Junjue Wang; Liangpei Zhang
The scene classification approaches using deep learning have been the subject of much attention for remote sensing imagery. However, most deep learning networks have been constructed with a fixed architecture for natural image processing, and they are difficult to apply directly to remote sensing images, due to the more complex geometric structural features. Thus, there is an urgent need for automatic
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Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2021-01-05 Mauro M. Barbat; Thomas Rackow; Christine Wesche; Hartmut H. Hellmer; Mauricio M. Mata
Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact
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Digital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone images ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-31 Jae-In Kim; Chang-Uk Hyun; Hyangsun Han; Hyun-Cheol Kim
Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To
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An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-29 Fei Su; Haihong Zhu; Taoyi Chen; Lin Li; Fan Yang; Huixiang Peng; Lei Tang; Xinkai Zuo; Yifan Liang; Shen Ying
Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can
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Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-30 Matthias Schlögl; Barbara Widhalm; Michael Avian
We present a comprehensive methodological framework for structural deformation monitoring of critical infrastructure assets based on differential SAR interferometry. By employing persistent scatterer interferometry, deformation time series in line-of-sight are derived from freely available Sentinel-1 single look complex products. These raw time series are analysed and refined using an extensive post-processing
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Multi-directional change detection between point clouds ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-21 Jack G. Williams; Katharina Anders; Lukas Winiwarter; Vivien Zahs; Bernhard Höfle
Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive,
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Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-19 Maryam Pourshamsi; Junshi Xia; Naoto Yokoya; Mariano Garcia; Marco Lavalle; Eric Pottier; Heiko Balzter
Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms
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A novel surface water index using local background information for long term and large-scale Landsat images ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-18 Linrong Li; Hongjun Su; Qian Du; Taixia Wu
Surface water plays a vital role in natural environment and human development. The research of water extraction method using remote sensing image is a hot topic, which has been widely developed in water index, classification, subpixel, and other aspects. Compared with other methods, a water-index based method has the advantages of fast speed and convenience. The characteristics of surface water, such
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Robust line feature matching based on pair-wise geometric constraints and matching redundancy ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-16 Jingxue Wang; Qing Zhu; Suyan Liu; Weixi Wang
This paper presents a novel method for matching line segments in images based on pair-wise geometric constraints and matching redundancy. In this study, pairs of line segments satisfying angle and distance constraints are used as matching primitives. To ensure that each extracted line segment is paired with another line segment, the search region of each line segment is gradually grown until it is
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Per-pixel land cover accuracy prediction: A random forest-based method with limited reference sample data ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-15 Hamid Ebrahimy; Babak Mirbagheri; Ali Akbar Matkan; Mohsen Azadbakht
Given the importance of accuracy in land cover (LC) maps, several methods have been adopted to predict per-pixel land cover accuracy (PLCA) of classified remote sensing images. Such a PLCA map provides spatially-explicit accuracy information and is of paramount importance for both producers and end-users of LC maps to thoroughly understand the spatial distribution of accuracy. In this study, we proposed
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Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-15 Rajeev Bhattarai; Parinaz Rahimzadeh-Bajgiran; Aaron Weiskittel; Aaron Meneghini; David A. MacLean
Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a
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Radiometric correction of laser scanning intensity data applied for terrestrial laser scanning ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-15 Nathan Sanchiz-Viel; Estelle Bretagne; El Mustapha Mouaddib; Pascal Dassonvalle
Alongside spatial information, an intensity scalar is also measured by LiDAR (Light Detection And Ranging) sensor systems. It corresponds to the amplitude of the backscattered laser beam after reflection on the scanned surface. This information isn’t directly usable due to dependencies of geometrical parameters occurring during the scanning process and additional processing modifications. The research
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Robust global registration of point clouds by closed-form solution in the frequency domain ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-10 Rong Huang; Yusheng Xu; Wei Yao; Ludwig Hoegner; Uwe Stilla
Point cloud registration is invariably an essential and challenging task in the fields of photogrammetry and computer vision to align multiple point clouds to a united reference frame. In this paper, we propose a novel global registration method using a robust phase correlation method for registration of low-overlapping point clouds, which is less sensitive to noise and outliers than feature-based
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Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-11 Jose A. Caparros-Santiago; Victor Rodriguez-Galiano; Jadunandan Dash
Vegetation phenology is considered an important biological indicator in understanding the behaviour of ecosystems and how it responds to environmental cues. Changes in vegetation dynamics have been strongly linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems, such as the land surface-atmosphere exchange of water and carbon, energy
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Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-11 Phuong D. Dao; Kiran Mantripragada; Yuhong He; Faisal Z. Qureshi
Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally
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A cross-correction LiDAR SLAM method for high-accuracy 2D mapping of problematic scenario ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-11 Shoujun Jia; Chun Liu; Hangbin Wu; Doudou Zeng; Mengchi Ai
Highly accurate 2D maps can supply basic geospatial information for efficient and accurate indoor building modeling. However, problematic scenarios, which are characterized by few features, similar components and large scales, seriously influence data association and cumulative error elimination, and thus degrade simultaneous localization and mapping (SLAM)-based mapping quality. In this paper, a cross-correction
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Three-dimensional photogrammetry with deep learning instance segmentation to extract berry fruit harvestability traits ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-10 Xueping Ni; Changying Li; Huanyu Jiang; Fumiomi Takeda
Fruit cluster characteristics such as compactness, maturity, berry number, and berry size, are important phenotypic traits associated with harvestability and yield of blueberry genotypes and can be used to monitor berry development and improve crop management. The goal of this study was to develop a complete framework of 3D segmentation for individual blueberries as they develop in clusters and to
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Missing data reconstruction in VHR images based on progressive structure prediction and texture generation ISPRS J. Photogramm. Remote Sens. (IF 7.319) Pub Date : 2020-12-09 Hanwen Xu; Xinming Tang; Bo Ai; Xiaoming Gao; Fanlin Yang; Zhen Wen
Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when dealing with VHR images with large-scale missing regions, existing spatial-based methods often destroy the structural information of ground objects. To alleviate this