bluuebunny/crossref_metadata_2025 huggingface dataset:
ABSOLUTE LOCALIZATION OF CORNER REFLECTORS WITH TERRASAR-X SPOTLIGHT IMAGES
Authors: Song R. , Wu J. , Li T. , Hu F.
Published: August 2020

Abstract. Synthetic aperture radar, capable of imaging the Earth surface from space in nearly all-weather conditions and high spatial resolution, has shown its outstanding capability for a variety of ground mapping applications. With well-controlled orbits of the new generation SAR satellites, high accuracy absolute localization with multiple SAR images has been demonstrated and become one of the hot spots with increasing attention. In this paper, high-resolution Spotlight-mode TerraSAR-X images acquired from the single orbit track were applied to 3D absolute positioning of three triangular trihedral corner reflectors. In order to overcome the limitation imposed by the acquisitions with very short baselines, a height constraint was introduced and the sub-meter accuracy was derived after carefully compensating for the known error sources, such as atmospheric delays and solid earth tide shifts.
ESRGAN-BASED DEM SUPER-RESOLUTION FOR ENHANCED SLOPE DEFORMATION MONITORING IN LANTAU ISLAND OF HONG KONG
Authors: Wu Z. , Ma P.
Published: August 2020

Abstract. Monitoring, evaluating and understanding the slopes by Interferometric Synthetic Aperture Rader (InSAR) technology are critical for both human economy and natural environment. However, the resolution limitation of existing digital elevation model (DEM) in the slope areas causes the DEM phase residues and atmospheric effects promoted, which will influence the interpret accuracy of InSAR results. In this study, we propose a novel two-step ESRGAN-based DEM SR method to effectively recover high-resolution DEM from the original version. Firstly, we pretrain an ESRGAN with a large number of natural images. Based on it, we transfer the learnt knowledge into the DEM problem and fine-tune the DEM SR network. The recovered DEMs are utilized as the reference data to improve slope deformation monitoring and enhance the accuracy of InSAR estimation, especially in the mountainous areas with cloudy and rainy weather. Experiments indicate that the proposed method can achieve better results than the traditional methods and works in phase simulation, which is one of the key step of InSAR deformation monitoring.
AN UNSUPERVISED LABELING APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: González Santiago J. , Schenkel F. , Gross W. , Middelmann W.
Published: August 2020

Abstract. The application of hyperspectral image analysis for land cover classification is mainly executed in presence of manually labeled data. The ground truth represents the distribution of the actual classes and it is mostly derived from field recorded information. Its manual generation is ineffective, tedious and very time-consuming. The continuously increasing amount of proprietary and publicly available datasets makes it imperative to reduce these related costs. In addition, adequately equipped computer systems are more capable of identifying patterns and neighbourhood relationships than a human operator. Based on these facts, an unsupervised labeling approach is presented to automatically generate labeled images used during the training of a convolutional neural network (CNN) classifier. The proposed method begins with the segmentation stage where an adapted version of the simple linear iterative clustering (SLIC) algorithm for dealing with hyperspectral data is used. Consequently, the Hierarchical Agglomerative Clustering (HAC) and Fuzzy C-Means (FCM) algorithms are employed to efficiently group similar superpixels considering distances with respect to each other. The distinct utilization of these clustering techniques defines a complementary stage for overcoming class overlapping during image generation. Ultimately, a CNN classifier is trained using the computed image to pixel-wise predict classes on unseen datasets. The labeling results, obtained using two hyperspectral benchmark datasets, indicate that the current approach is able to detect objects boundaries, automatically assign class labels to the entire dataset and to classify new data with a prediction certainty of 90%. Additionally, this method is also capable of achieving better classification accuracy and visual correspondence with reality than the ground truth images.
RANDOM PROJECTION BASED BIAS-CORRECTED FUZZY C-MEANS ALGORITHM FOR HYPERSPECTRAL REMOTE SENSING IMAGE SEGMENTATION
Authors: Jia S. , Zhao Q. , Wang L. , Li Y.
Published: August 2020

Abstract. To address the issue of the information redundancy for hyperspectral remote sensing image, this paper presents a novel ensemble algorithm that merges Random Projection (RP) and Bias-corrected Fuzzy C-means (BCFCM) algorithm. Since RP matrix has the abilities of preserving information nicely, it can be used to reduce the dimension of the image. To make full advantage of neighborhood relationship, BCFCM algorithm is improved to segment the low-dimensional image, in which Euclidean distances are retained to define the similarity between hyperspectral remote sensing image and the low-dimensional image. Finally, BCFCM algorithm is used to segment the fuzzy membership matrix of the ensemble algorithm. The proposed algorithm is evaluated by real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral remote sensing images. Segmentation performance is estimated by kappa coefficient and overall accuracy. Experimental results demonstrate that the proposed algorithm can achieve higher segmentation accuracy at a lower computational cost than that from conventional algorithms.
DRONE DATA ATMOSPHERIC CORRECTION CONCEPT FOR MULTI- AND
HYPERSPECTRAL IMAGERY – THE DROACOR MODEL
Authors: Schläpfer D. , Popp C. , Richter R.
Published: August 2020

Abstract. Remote sensing with unmanned aerial vehicles (UAVs) is a fast and cost-efficient tool for mapping and environmental monitoring. The sensors are operated at low flight altitudes, usually below 500 m above ground, leading to spatial resolutions up to the centimeter range. This type of data causes new challenges in atmospheric compensation and surface reflectance retrieval. Based on these specific boundary conditions, a new drone based atmospheric correction concept (DROACOR) is proposed, which is designed for currently available UAV based sensors. It is suited for multispectral visible/near infrared sensors as well as hyperspectral instruments covering the 400–1000 nm spectral region or the 400–2500 nm spectrum. The goal of the development is a fully automatic processor which dynamically adjusts to the given instrument and the atmospheric conditions. Optionally, irradiance measurements from simultaneously measured cosine receptors or from in-field reference panels can be taken into account to improve the processing quality by adjusting the irradiance parameter or by performing an in-flight vicarious calibration. Examples of DROACOR processing results are presented for a multispectral image data set and a hyperspectral data set, both acquired at variable flight altitudes. The resulting spectra show the applicability of the methods for both sensor types and an accuracy level below 2.5% reflectance units.
VEHICLE DETECTION IN HIGH RESOLUTION IMAGE BASED ON DEEP LEARNING
Authors: Gao H. , Li X.
Published: August 2020

Abstract. Despite its high accuracy and fast speed in object detection, Single Shot Multi-Box Detector (SSD) tends to get undesirable results especially for small targets such as vehicles on high-resolution images. In this paper, we propose a new convolutional neural network based on SSD to detect vehicles on high-resolution images. In the proposed framework, the feature fusion module and detection module are incorporated. In the feature fusion module, feature maps of different scales are integrated into a fusion feature for object detection, which could improve the accuracy effectively. Besides, to prevent the network from overfitting and speed up the training, the batch normalization layer is embedded between the detection layers in the detection module. Some ablation experiments provide strong evidence for the effectiveness of these above structures. On the UCAS-High Resolution Aerial Object Detection Dataset, our network has the ability to achieve the 0.904 AP (average precision) with 0.094 AP higher than SSD512 but similar speed to it.
STACKED LOCAL FEATURE DETECTOR FOR HYPERSPECTRAL IMAGE
Authors: Yan Z. , Wu Z.
Published: August 2020

Abstract. Images registration is an important task in hyperspectral image processing, while almost all local feature point based image matching algorithm is designed for single band image only and miss the advantage of abundant spectral information. Therefor, in this paper we propose a novel local feature detector for hyperspectral image. This method, which is named stacked local feature detector (HSI-SFD), stack all local feature points detected from every single spectral band. With redundant information, local feature miss-detected at noise pixel can be filtered out by the stack count threshold, leading to more reliable and robust local features. To verify the algorithm, a hyperspectral image matching dataset is built. Multiview hyperspectral images of the several different flat targets are taken by scan-line hyperspectral camera in library. Each image contains 270 bands within 400–1000 nm. Groundtruth transformation matrix between images is computed from corresponding points selected by hands. Experiment on the dataset shows that features points miss-detected at texture-less area can be inhibited by HSI-SFD. Both keypoint repeatability and matching accuracy increase significantly. Precision and matching score increase up to 10% for some scene.
A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR
Authors: Chen B. , Shi S. , Gong W. , Sun J. , Chen B. , Guo K. , Du L. , Yang J. , Xu Q. , Song S.
Published: August 2020

Abstract. Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.
POINT CLOUD TRANSFORMATION USING SENSOR CALIBRATION INFORMATION FOR MAP DATA ADJUSTMENT
Authors: Lee J. , Bae J. , Choi Y. , Park I. , Hong S. , Sohn H.
Published: August 2020

Abstract. In order to operate autonomous vehicles and unmanned delivery vehicle, it is important to accurately acquire location of the device itself. However, since these devices are mainly operated in urban areas, there is a limit in obtaining location information based on GNSS. Therefore, it is necessary to utilize a method of calibrating its own location information by measuring the reference point provided by the existing high-precision map of the region. Point cloud based multi-dimensional high-precision maps are acquired in advance using high-performance LiDAR and GNSS devices for infrastructure such as roads, and provide a reference point for autonomous driving or map updating. Since such high-performance surveying equipment requires high cost, it is difficult to attach to autonomous vehicles or unmanned vehicle for commercialization. Therefore, autonomous vehicles or unmanned delivery vehicle are operated with relatively low performance LiDAR and GNSS, so it is often impossible to accurately measure the reference point, which directly leads to a decrease in the accuracy of the location information of the device. To compensate for this, this study proposes a point interpolation method to extract GCP information from sparse point cloud maps acquired with low performance LiDAR. The proposed method uses calibration parameters between point data and the image data acquired from the device. In general, images provide higher resolution than point clouds, even when using low-end cameras, so that the position of point coordinates relative to a reference point can be measured relatively accurately from the image and projection data of the point cloud. The data acquisition vehicle is an MMS vehicle that provides a panoramic image using four DSLRs and a point cloud with Velodyne VLP 16. The researchers first conducted a reference point survey on features such as road signs. The panorama image including the road sign was transformed into a bird eye’s view, and point projection was performed on the bird eye’s view image. The reference point coordinates, which were not acquired by the point cloud, were obtained from the shape of the road sign in the bird eye’s view image, and the accuracy was compared with the measured data.
EFFICIENT LARGE-SCALE AIRBORNE LIDAR DATA CLASSIFICATION VIA FULLY CONVOLUTIONAL NETWORK
Authors: Maset E. , Padova B. , Fusiello A.
Published: August 2020

Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.
PRELIMINARY RESULTS AND ANALYSES OF POST-EARTHQUAKE GEOLOGICAL HAZARDS IN JIUZHAIGOU BASED ON AIRBORNE LIDAR AND IMAGERY
Authors: She J. , Zhou X. , Liu F. , Cheng D. , Liao L.
Published: August 2020

Abstract. A magnitude 7.0 earthquake struck Jiuzhaigou County, Sichuan Province at 21:00 on August 8, 2017, and the epicentre was located in the major tourist spot of Jiuzhaigou. This paper describes the surveying and mapping project of post-earthquake hazards regions for the restoration and reconstruction purposes. The earthquake triggered thousands of collapses and landslide disasters, causing severe damage to tourist land scapes and scenic roads such as Jiuzhaigou Panda Sea. The geological hazards of steep mountains and steep slopes and dense vegetation in the scenic area are highly concealed. Traditional susceptibility mapping methods for geological hazard using only airborne images have encountered great difficulties. Aiming to solve this issue, airborne LiDAR and high-resolution images were used jointly to interpret geological disasters in this area. The three-dimensional interpretation signs of disasters are established comprehensively by using optical images and digital elevation models to obtain the distribution characteristics of existing geological hazards, coseismic geological hazards and hidden dangers, which support the restoration and reconstruction of the tourist spots.
HIGH-PRECISION RANGING BASED ON MULTISPECTRAL FULL-WAVEFORM LIDAR
Authors: Wang B. , Song S. , Gong W. , Shi S. , Chen B. , Yang J. , Du L. , Sun J.
Published: August 2020

Abstract. Full-waveform light detection and ranging (LiDAR) has become a well-established technique for remote sensing of surface topography and target characterization, offering numerous opportunities in investigation and interpretation of the structural diversity of surface coverage. However, most prevailing waveform decomposition methods employ the Gaussian function or some other probability distributions to model LiDAR waveforms of specific shapes, which cannot be universally used. Moreover, most of these waveform decomposition methods operate at a single laser wavelength and cannot be applied well to multispectral LiDAR (MSL) or hyperspectral LiDAR (HSL), which can simultaneously collect the spectral and geometric attributes with multiple transmitting laser wavelengths. In this paper, we propose a new multispectral B-spline waveform decomposition model to achieve high precision multi-target ranging. Considering both the spatial consistency of each channel and the irregular shape of the received waveform, LiDAR waveforms are modelled by B-splines rather than any other specific probability distribution. Thus, the proposed method can be extended to other FWMSL data benefiting from the flexibility of B-splines on fitting arbitrary curves. Both simulated MSL echoes and a measured dataset from a FWMSL system with three wavelengths of 556, 670, and 780 nm were used in this study. Compared with two single wavelength waveform decomposition models and a multispectral waveform decomposition model based on Gaussian function, the proposed method has excellent robustness for processing different shapes of waveforms and can improve ranging accuracy significantly for irregular waveforms.
A PHYSICAL INVERSION METHOD OF CANOPY FPAR FROM AIRBORNE LIDAR DATA AND GROUND MEASUREMENTS
Authors: Yang X. , Xi X. , Wang C. , Shi J. , Huang Y.
Published: August 2020

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.
PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION
Authors: Ajayi O. G.
Published: August 2020

Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.
AUTOMATIC TRAFFIC SIGN DETECTION AND RECOGNITION USING MOBILE LIDAR DATA WITH DIGITAL IMAGES
Authors: Guan H. , Yu Y. , Li D. , Li J.
Published: August 2020

Abstract. This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest regions are first extracted from mobile LiDRA data. Next, traffic signs are identified from digital images simultaneously collected from the multi-sensor mobile LiDAR systems via a convolutional capsule network model. The experimental results demonstrate that the proposed method obtains a promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.
SPACEBORNE GNSS-R RETRIEVING ON GLOBAL SOIL MOISTURE APPROACHED BY SUPPORT VECTOR MACHINE LEARNING
Authors: Lwin A. , Yang D. , Hong X. , Cheraghi Shamsabadi S. , Ahmed W. A.
Published: August 2020

Abstract. GNSS Reflectometry system is an excellent to sense soil moisture content. In recent, GNSS-R technique could be aided to detect soil moisture contents but still have many difficulities issues, most especially vegetation impact. Soil moisture observing is a major concept for enhancing the sustainability of the earth’s system and process. On retrieving soil moisture from spaceborne GNSS-R technology has been challenging to the system, retrieving model and geophysical parameters. In this research, we use the Support Vector Machine (SVM) method to retrieve global soil moisture, the TDS-1 Delay Doppler Map (DDM) and the AVHRR Normalized Difference Vegetation Index (NDVI) imagery as inputs and the Soil Moisture and Ocean Salinity (SMOS) soil moisture data as a reference to retrieve global SM daily basis. The results have shown that the squared correlation coefficient (R) values are much higher in TDS-1 fused with NDVI than using DDM alone, which indicates that vegetation impact has effectively weakened. The feasibility of this approach could provide the performance for spaceborne GNSS-R retrieving to soil moisture analysis.
SEMI-AUTOMATIC APPROACH FOR OPTICAL AND LIDAR DATA INTEGRATION USING PHASE CONGRUENCY MODEL AT MULTIPLE RESOLUTIONS
Authors: Megahed Y. , Yan W. Y. , Shaker A.
Published: August 2020

Abstract. In light of the ongoing urban sprawl reported in recent studies, accurate urban mapping is essential for assessing current status and evolve new policies, to overcome various social, environmental, and economic consequence. Imagery and LiDAR data integration densifies remotely sensed data with radiometric and geometric characteristics, respectively, for a precise segregation of different urban features. This study integrated aerial and LiDAR images using point primitives, which were obtained from running the Phase Congruency model as an image filter to detect edges and corner. The main objective is to study the effect of applying the filter at different spatial resolutions on the registration accuracy and processing time. The detected edge/corner points that are mutual in both datasets, were identified as candidate points. The Shape Context Descriptor method paired-up candidate points as final points based on a minimum correlation of 95%. Affine, second and third order polynomials, in addition to the Direct Linear Transformation models were applied for the image registration process using the two sets of final points. The models were solved using Least Squares adjustments, and validated by a set of 55 checkpoints. It was observed that with the decrease in spatial resolution, on one hand, the registration accuracy did not significantly vary. However, the consistency of the model development and model validation accuracies were enhanced, especially with the third order polynomial model. On the other hand, the number of candidate points decreased; consequently, the processing time significantly declined. The 3D LiDAR points were visualised based on the Red, Green, and Blue radiometric values that were inherited from the aerial photo. The qualitative inspection was very satisfactory, especially when examining the scene’s tiny details. In spite of the interactivity in determining the candidate points, the proposed procedure overcomes the dissimilarity between datasets in terms of acquisition technique and time, and widens the tolerance of accepting points as candidates by including points that are not traditionally considered (i.e. road intersections).
THE EXTRACTION OF URBANIZED AREAS THROUGH IMAGES OF HIGH RESOLUTION NIGHTTIME LIGHTS
Authors: Arellano B. , Roca J.
Published: August 2020

Abstract. Satellite nocturnal images of the earth are a useful way to identify urbanisation. Nighttime lights have been used in a variety of scientific contributions, including studies on the identification of metropolitan areas as well as landscapes impacted by urbanization. However, the study of urban systems by nighttime light imagery has had a fundamental limitation to date: the low spatial resolution of satellite sensors. Although the DMSP Operational Linescan System (OLS) has been gathering global low-light imaging data for over 40 years, its 2.7 km/pixel footprint has limited its use for in-depth studies of urban development. The 2011 launch by NASA and the NOAA of the Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, has led to a significant improvement. This instrument has better spatial resolution (742 m/pixel), on-board calibration, a greater radiometric range, and fewer saturation and blooming problems than DMSP-OLS data. However, it still has considerable limitations for the in-depth study of the area and internal structure of urban systems.The launch of Luojia 1-01 in June 2018 has increased expectations. LJ1-01 is a nano satellite that can obtain high-resolution nocturnal images (130 metres/pixel). The aim of this paper is to analyse, and compare with previous satellites, the new instrument’s capacity to delimit the urbanised area and its efficiency in identifying types of urban landscape (compact, dispersed and rurban). The study cases are Barcelona Metropolitan Region (Spain) and Shenzhen City (China).
SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA
Authors: Belcore E. , Piras M. , Wozniak E.
Published: August 2020

Abstract. Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945.
PREFACE: TECHNICAL COMMISSION III
Authors: Jiang J. , Shaker A. , Zhang H.
Published: August 2020

Abstract. The work of ISPRS Technical Commission III is devoted to remote sensing. For the XXIVth ISPRS CONGRESS, Technical Commission III received total 603 submissions 216 full papers and 387 abstracts. Among these submissions 107 are accepted as peer-reviewed contributions for publication in the ISPRS Annals, 218 were accepted for publication in the ISPRS Archives. There are 228 accepted submissions were withdrawn by authors due to the postpone of the Congress and other reasons.These papers are dedicated mostly to topics of the 10 TC III working groups and 4 inter-commission working groups as follows – WG III/1: Thematic Information Extraction; WG III/2: Microwave Remote Sensing; WG III/3: SAR-based Surface Generation and Deformation Monitoring; WG III/4: Hyperspectral Image Processing; WG III/5: Information Extraction from LiDAR Intensity Data; WG III/6: Remote Sensing Data Fusion; WG III/7: Landuse and Landcover Change Detection; WG III/8: Remote Sensing of Atmospheric Environment; WG III/9: Cryosphere and Hydrosphere; WG III/10: Agriculture and Natural Ecosystems Modelling and Monitoring; ICWG III/II: Planetary Remote Sensing and Mapping; ICWG III/Iva: Disaster Assessment, Monitoring and Management; ICWG III/IVb: Remote Sensing Data Quality; ICWG III/IVc: Environment and Health. The papers and abstracts were evaluated by the experts in the field and Working Group Chairs according to content, significance, originality, relevance, and clearness of presentation.We would like to thank the authors for their contributions, the reviewers for their reviewing, the working group officers for their efforts on calling for papers, and the organizers of the Congress for publishing this volume.
CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS AND EFFECT OF NUMERICAL ITERATIONS, IMAGE INTERVAL (TIME STEPS) ON PREDICTION RESULTS
Authors: Mondal M. S. , Sharma N. , Kappas M. , Garg P. K.
Published: August 2020

Abstract. In this study, land use land cover (LULC) map of 1987, 1997 and 2007 derived from digital satellite images of Landsat - 5 TM of 1987, IRS-1C LISS III images of 1997 and IRS-P6 LISS III images of 2007, respectively, to monitor and predict future LULC scenario. Future LULC has been predicted using CA Markov Model for 2017, 2027 and 2050 by using LULC map of 1987 & 1997, 1997 & 2007 and 1987 & 2007. The period (image interval / time steps) between different predictions years (i.e., 2017, 2027 and 2050 using 1987 & 1997, 1997 & 2007 and 1987 & 2007 LULC) are 10, 20, 30, 43 and 53 years. The number of iteration was based on the time steps i.e., iterations 20 to predict LULC for 2017 (prediction from 1997 to 2017); iterations 30 for 2027 (prediction from 1997 to 2027); iterations 53 for 2050 (prediction from 1997 to 2050). The relationships (correlations) of quantity of predicted LULC found strong positive correlation for three time periods (0.981, 0.984, 0.966 for 2017, 0.981, 0.984, 0.975 for 2027, 0.977, 0.987, and 0.980 for 2050) and established that there are almost no effect in quantity of prediction results of different time steps (iterations) images and time intervals are used to predict future LULC. The predicting location of predicted LULC of 2017, 2027 & 2050 for the three cases showing positive correlation, where r are 0.728, 0.758 and 0.708 for 2017 – when relatively less time steps used; r are 0.696, 0.761 and 0.674 for 2027 – when medium time steps used; r are 0.599, 0.721, 0.574 for 2050 – when more time steps used. The analysis established that although there are nearly no effect on quantitative prediction results but have small impact of iterations (time steps) and time intervals on spatial distribution of predicted LULC results.
REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)
Authors: Myagmartseren P. , Ganpurev D. , Myagmarjav I. , Byambakhuu G. , Dabuxile G.
Published: August 2020

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI & TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.
ROTATION FORESTS AND RANDOM FOREST CLASSIFIERS FOR MONITORING OF VEGETATION IN PAYS DE BREST (FRANCE)
Authors: Niculescu S. , Xia J. , Roberts D. , Billey A.
Published: August 2020

Abstract. Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. The latest advances, in particular the arrival of new image acquisition programs, are changing the temporal approach to monitoring vegetation. The latest European satellites launched, delivering an image every 5 days for each point on the globe, allow the end of a growing season to be monitored. The main objective of this work is to identify and map the vegetation in the Pays de Brest area by using a multi sensors stacking of Sentinel-1 and Sentinel-2 satellites data via Random Forest, Rotation forests (RoF) and Canonical Correlation Forests (CCFs). RoF and CCF create diverse base learners using data transformation and subset features. Twenty four radar images and optical dataa representing different dates in 2017 were processed in time series stacks. The results of RoF and CCF were compared with the ones of RF.
DETECTION OF LAND COVER DISPLACEMENTS THROUGH TIME-SERIES ANALYSIS OF MULTISPECTRAL SATELLITE IMAGERY: APPLICATION TO DESERT
Authors: Oxoli D. , Brovelli M. A. , Frizzi D. , Martinati S.
Published: August 2020

Abstract. Detection of changes occurring on Earth surface has become an established practice in remote sensing science since the availability of high-resolution, global coverage, and multitemporal satellite imagery that has constantly increased during the last decades. Meanwhile, the open data policies embraced by some of the principal Earth Observation programs have boosted the spread of such analyses. This asset has attracted also the interest of the private sector which is developing strategies to exploit the potential of these data. In view of the above, we present an experimental procedure to investigate land cover displacements through an application on open multispectral imagery from the Landsat 8 and Sentinel-2 missions for desert sand dunes movements analysis. While most of the change detection techniques focus on locating changes and describing them by means of variation in the pixel spectral responses, the proposed technique aims at describing spatial and temporal patterns of displacements (i.e. directions and magnitude) applying cross-correlation analysis on a multitemporal images stack. Results of the proposed analysis are critical to a number of construction engineering operations that require sand mitigation planning and continuous site monitoring to prevent windblown sand interactions with infrastructures in the desert environment. An overview of the preliminary results is presented together with an extensive discussion on further improvements requested by the procedure. Computational steps leverage exclusively open data and free and open source GIS software thus providing large rooms to empower, replicate and improve thereof.
SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS
Authors: He L. , Wu Z. , Zhang Y. , Hu Z.
Published: August 2020

Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.
INVESTIGATION OF 2019 RAINFALL EFFECTS ON URMIA LAKE SURFACE AND EXTRACTION OF LAKE SHORELINE CHANGES AND COMPARISON WITH THE PREVIOUS DECADE USING REMOTE SENSING IMAGES AND GIS
Authors: Shandi Z. B. , Helali H.
Published: August 2020

Abstract. Urmia Lake has experienced many fluctuations during recent decades, which played a key role in the socio-economic changes in the north-western part of Iran. Consequently, socio-economic planning and land management around the lake require anticipation of changes’ trend. In this paper, regarding continual rainfall in the beginning of 2019, shoreline and water level changes of this lake have been studied and compared to the previous decade. To this aim, Landsat satellite imagery (Oli, ETM and TM sensors’ images) was used to extract NDWI index by using Green and NIR bands. The results of this study showed that Urmia Lake has declined over the past ten years and reached its lowest level in the year 2015. However, due to rainfall of 2019, these changes have had ascending trend which made water area equal to the situation in 2010. It, also was observed that similar to the trend of water level, the shoreline has progressed toward the Lake from the east and the south and become salt marsh. But, the West and the North parts have not changed significantly. Then, in 2019 salt marsh lands have been submerged once again, and the vast eastern island, which was completely blended in with the surrounding lands, returned to its previous state, the peninsula. In addition, considering the trend of rainfall and Lake’s restoring activities, the minimum and maximum time required to reach the area in 2010 were estimated 23 and 38 years respectively, assuming the volume of precipitation remains constant and the reduction of these activities at a constant rate.
AEROSOL PLUMES CHARACTERIZATION BY HYPERSPECTRAL IMAGES COUPLED WITH SENTINEL-2 PRODUCTS
Authors: Calassou G. , Foucher P.-Y. , Leon J.-F.
Published: August 2020

Abstract. In this paper, we focus on the retrieval of microphysical and optical properties of industrial aerosol plumes through a process using airborne hyperspectral and Sentinel-2 multi-spectral images. The process allows first to perform atmospheric correction and second to determine background aerosols thanks to a comparison between hyperspectral and Sentinel-2 reflectances. Hyperspectral methodologies use the radiance differential between the measurement in the plume and the corresponding measurements out of the plume to estimate plume properties. To retrieve the surface reflectance under the plume, a principal component analysis coupling hyperspectral and multispectral data class by class is achieved. The developed method aims to compare the difference between measured and estimated reflectance with a radiative transfer model accounting for plume properties (particle radius and aerosol optical thickness of the plume). We have applied the method to a steel plant in the south of France. The retrieved plume show an aerosol mean radius between 0.05 and 0.2 µm with a mean aerosol optical thickness about 0.05 along the plume.
DETECTION AND QUANTIFICATION OF INDUSTRIAL METHANE PLUME WITH THE AIRBORNE HYSPEX-NEO CAMERA AND APPLICATIONS TO SATELLITE DATA
Authors: Nesme N. , Foucher P.-Y. , Doz S.
Published: August 2020

Abstract. The control and monitoring of greenhouse gases is an important issue for the study of climate change, for the impact in terms of public health or for the risks related to industry. An algorithm has been developped dedicated to commercial airborne hyperspectral camera as HySpex-NEO for the detection of industrial methane plume and the quantification of the emission source. HySpex-NEO is an imager used in airborne campaign with a spatial resolution of 1.4 m at flight altitude of 2 km, a swath of 650 m and a spectral resolution of 6 nm. This algorithm has been validated over a controlled release less than 100 g/s during an airborne campaign over Lacq (France) industry. It has also been applied to the Aliso Canyon leakage data acquired with AVIRIS JPL (Airborne Visible Infrared Spectrometer) with a spatial resolution of 6.8 m at flight altitude of 6 km, a swath of 5.6 km and a spectral resolution of 10 nm. Application to satellite hyperspectral data is shown on artificial data derived from airborne hyperspectral acquisitions.
INTRODUCTION OF CLOUDS IN DART MODEL
Authors: Wang Y. , Grimaldi J. , Landier L. , Chavanon E. , Gastellu-Etchegorry J. P.
Published: August 2020

Abstract. Clouds cover around two thirds of the Earth’s surface. Most of them are thick enough to influence the radiative budget of our planet: they increase the top of atmosphere (TOA) exitance and they alter the bottom of atmosphere (BOA) direct and diffuse irradiance. However, most radiative transfer models dedicated to Earth surfaces, such as DART (Discrete Anisotropic Radiative Transfer), simulate only cloudless atmospheres. We recently introduced clouds in DART in order to improve the modelling of weather for remote sensing simulations. In this implementation, clouds were characterized with user specified optical properties and vertical distribution. They were modelled as layered one-dimensional medium that coexists with gases and aerosols. The atmospheric radiative transfer modelling relies on the discrete ordinate method already in DART. In addition, an iterative inversion procedure was designed to test this improvement with field measurements during two cloudy days at Lamasquère meteorological station (France). Specifically, it derives time-series of atmosphere parameters from time-series of BOA solar irradiance measurements. These inversed atmospheric parameters were used to simulate total and diffuse BOA irradiance in PAR (Photosynthetically Active Radiation) domain. The comparison of time-series of measured and DART simulated PAR irradiance lead to very encouraging results (mean relative error ∼8% for total irradiance and ∼20% for diffuse irradiance). It stresses the potential of DART to accurately simulate irradiance in cloudy days.
PRELIMINARY LONG-PERIOD MAGNETOTELLURIC INVESTIGATION AT THE EDGE OF ICE SHEET IN EAST ANTARCTICA
Authors: Guo J. , Wang K. , Zeng Z. , Li L. , Liu J. , Tang X. , Cui X. , Wang Y. , Sun B. , Zhang J.
Published: August 2020

Abstract. The lithospheric mantle structure of the Antarctic continent is of great significance of studying the polymerization and fragmentation mechanism of Gondwana and the plate movement law. Long-period magnetotelluric (LMT) is an important method to study the electrical structure of earth crust and mantle. However, been limited by the bad natural environment and logistics supply difficulties, there is no LMT record of Antarctica before. In 2018, China's 34th Antarctic scientific expedition carried out the LMT survey at the eastern edge of the Antarctic continent with a frequency range of 0.00015 Hz to 0.1 Hz. After the processing and analysis, we get three points as fellow: (1) The lithospheric mantle of Antarctica has a three-dimensional resistivity structure; (2) There are low resistivity regions in the Antarctic mantle, which may be related to thermal activity. (3) It is possible to carry out LMT measurements in eastern Antarctic and more can be done in the future.
STUDY ON ARCTIC MELT POND FRACTION RETRIEVAL ALGORITHM USING MODIS DATA
Authors: Su J. , Yu P. , Qin Y. , Zhang G. , Wang M.
Published: August 2020

Abstract. During spring and summer, melt ponds appear on the sea ice surface in the Arctic and play an important role in sea ice-albedo feedback effect. The melt pond fraction (MPF) can be retrieved using multi-band linear equations, but the calculation is complicated by the ill-conditioned reflectance matrix. In this paper, we calculated the condition numbers which represent the degree of the ill-conditioned reflectance matrix in the results of the MPF from a MODIS-based unmixing algorithm. The condition number is introduced here as a criterion for the sensitivity of the solution in the system to the error in the input value. By combining 3 bands among 5 visible and near-infrared bands of MODIS data, the results show that the three-band combination with the lowest sensitivity to the error of input is B245. To improve the algorithm, we introduce pre-processing to remove open water from the four surface types and then remove one reflectance equation from the original set. The best two-band combination algorithm is B15. Compared with the discrimination results from Landsat5-TM, the RMS is 0.14. This algorithm is applied in pan-Arctic scale, the MPF results are larger than that from University of Hamburg, especially in the Pacific sector.