bluuebunny/crossref_metadata_2025 huggingface dataset:
VEHICLE DETECTION IN REMOTE SENSING IMAGES USING DEEP NEURAL NETWORKS AND MULTI-TASK LEARNING
Authors: Cao M. , Ji H. , Gao Z. , Mei T.
Published: August 2020

Abstract. Vehicle detection in remote sensing image has been attracting remarkable attention over past years for its applications in traffic, security, military, and surveillance fields. Due to the stunning success of deep learning techniques in object detection community, we consider to utilize CNNs for vehicle detection task in remote sensing image. Specifically, we take advantage of deep residual network, multi-scale feature fusion, hard example mining and homography augmentation to realize vehicle detection, which almost integrates all the advanced techniques in deep learning community. Furthermore, we simultaneously address super-resolution (SR) and detection problems of low-resolution (LR) image in an end-to-end manner. In consideration of the absence of paired low-/highresolution data which are generally time-consuming and cumbersome to collect, we leverage generative adversarial network (GAN) for unsupervised SR. Detection loss is back-propagated to SR generator to boost detection performance. We conduct experiments on representative benchmark datasets and demonstrate that our model yields significant improvements over state-of-the-art methods in deep learning and remote sensing areas.
DECOMPOSING IMAGES INTO TRIANGLES BY DELAUNAY POINT PROCESSES
Authors: Chai D.
Published: August 2020

Abstract. We propose a method for decomposing images into triangles. Contrary to superpixel methods, our output representation both preserves the geometric information disseminated in input images, and has an attractive storage capacity. Our method relies on the flexibility and efficiency of Delaunay point processes to address the problem. These stochastic models distribute points interacting between each other through Delaunay triangulations. The mechanism for distributing points combines several complementary ingredients including image discontinuity preservation, radiometric homogeneity inside atomic regions as well as priors on the shape of these regions. Said differently, sampled points and induced shapes work in tandem. The potential of our approach is shown through comparisons with existing oversegmentation methods and applications to vision problems.
TEMPORAL REPETITION DETECTION FOR GROUND VISIBILITY ASSESSMENT
Authors: Grompone von Gioi R. , Hessel C. , Dagobert T. , Morel J. M. , de Franchis C.
Published: August 2020

Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.
CORRECTION OF SYSTEMATIC RADIOMETRIC INHOMOGENEITY IN SCANNED AERIAL CAMPAIGNS USING PRINCIPAL COMPONENT ANALYSIS
Authors: Lelégard L. , Le Bris A. , Giordano S.
Published: August 2020

Abstract. Orthophotomosaic is defined as a single image that can be layered on a map. The term “mosaic” implies that it is produced from a set of images, usually aerial images. Even if these images are taken during cloudless period, they are impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as imaging device systematisms, like lens vignetting. These create some unsightly radiometric inhomogeneity in the orthophotomosaic that could be corrected by using a Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This work introduces an alternative to Wallis filter by considering some systematic radiometric behaviours in the images through a principal component analysis process.
CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES
Authors: Tadros A. , Drouyer S. , Grompone von Gioi R. , Facciolo G. , Carvalho L.
Published: August 2020

Abstract. Circle and ellipse detection have been a widely discussed topic among the computer vision community. Applying circle detection methods to satellite images can bring valuable information on urban or industrial areas. A limitation of such methods lies in the resolution of the object to detect. The smaller they are in the image, the harder it becomes to detect them accurately. In this paper, we explore several circle detection methods adapted to low resolution satellite images. An algorithm based on level-lines detection and classification will be presented in this paper along with other well known algorithms. The methods are evaluated in the context of oil tank detection in Sentinel-2 images where those circular objects can have an observed diameter as small as 2 pixels.
A HYBRID GLOBAL IMAGE ORIENTATION METHOD FOR SIMULTANEOUSLY ESTIMATING GLOBAL ROTATIONS AND GLOBAL TRANSLATIONS
Authors: Wang X. , Xiao T. , Kasten Y.
Published: August 2020

Abstract. In recent years, the determination of global image orientation, i.e. global SfM, has gained a lot of attentions from researchers, mainly due to its time efficiency. Most of the global methods take relative rotations and translations as input for a two-step strategy comprised of global rotation averaging and global translation averaging. This paper by contrast presents a hybrid approach that aims to solve global rotations and translations simultaneously, but hierarchically. We first extract an optimal minimum cover connected image triplet set (OMCTS) which includes all available images with a minimum number of triplets, all of them with the three related relative orientations being compatible to each other. For non-collinear triplets in the OMCTS, we introduce some basic characterizations of the corresponding essential matrices and solve for the image pose parameters by averaging the constrained essential matrices. For the collinear triplets, on the other hand, the image pose parameters are estimated by relative orientation using the depth of object points from individual local spatial intersection. Finally, all image orientations are estimated in a common coordinate frame by traversing every solved triplet using a similarity transformation. We show results of our method on different benchmarks and demonstrate the performance and capability of the proposed approach by comparing with other global SfM methods.
CULTURAL HERITAGE IN SMART CITY ENVIRONMENTS: THE UPDATE
Authors: Angelidou M. , Stylianidis E.
Published: August 2020

Abstract. In 2017 we published a seminal research study in the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences about how smart city tools, solutions and applications underpinned historical and cultural heritage of cities at that time (Angelidou et al. 2017). We now return to investigate the progress that has been made during the past three years, and specifically whether the weak substantiation of cultural heritage in smart city strategies that we observed in 2017 has been improved. The newest literature suggests that smart cities should capitalize on local strengths and give prominence to local culture and traditions and provides a handful of solutions to this end. However, a more thorough examination of what has been actually implemented reveals a (still) rather immature approach. The smart city cases that were selected for the purposes of this research include Tarragona (Spain), Budapest (Hungary) and Karlsruhe (Germany). For each one we collected information regarding the overarching structure of the initiative, the positioning of cultural heritage and the inclusion of heritage-related smart city applications. We then performed a comparative analysis based on a simplified version of the Digital Strategy Canvas. Our findings suggest that a rich cultural heritage and a broader strategic focus on touristic branding and promotion are key ingredients of smart city development in this domain; this is a commonality of all the investigated cities. Moreover, three different strategy architectures emerge, representing the different interplays among the smart city, cultural heritage and sustainable urban development. We conclude that a new generation of smart city initiatives is emerging, in which cultural heritage is of increasing importance. This generation tends to associate cultural heritage with social and cultural values, liveability and sustainable urban development.
GENERALIZED KNOWLEDGE DISTILLATION FOR MULTI-SENSOR REMOTE SENSING CLASSIFICATION: AN APPLICATION TO LAND COVER MAPPING
Authors: Ienco D. , Gbodjo Y. J. E. , Gaetano R. , Interdonato R.
Published: August 2020

Abstract. Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones) from all the different sensors due to: (i) atmospheric conditions and/or (ii) acquisition cost. In this context of data (or modalities) misalignment, only part of the area under consideration could be covered by the different sensors (modalities). Unfortunately, standard machine learning approaches commonly employed in operational Earth monitoring systems require consistency between training and test data (i.e., they need to match the same information schema). Such a constraint limits the use of additional fruitful information, i.e., information coming from a particular sensor that may be available at training but not at test time. Recently, a framework able to manage such information misalignment between training and test information is proposed under the name of Generalized Knowledge Distillation (GKD). With the aim to provide a proof of concept of GKD in the context of multi-source Earth Observation analysis, here we provide a Generalized Knowledge Distillation framework for land use land cover mapping involving radar (Sentinel-1) and optical (Sentinel-2) satellite image time series data (SITS). Considering that part of the optical information may not be available due to bad atmospheric conditions, we make the assumption that radar SITS are always available (at both training and test time) while optical SITS are only accessible when the model is learnt (i.e., it is considered as privileged information). Evaluations are carried out on a real-world study area in the southwest of France, namely Dordogne, considering a mapping task involving seven different land use land cover classes. Experimental results underline how the additional (privileged) information ameliorates the results of the radar based classification with a main gain on the agricultural classes.
ANALYSIS OF THE GEOMETRY OF SURFACE DEFORMATIONS CAUSED BY INDUCED TREMORS IN THE AREA OF UNDERGROUND COPPER MINING
Authors: Owczarz K. , Blachowski J.
Published: August 2020

Abstract. Induced seismicity by human operations such as mining is usually unpredictable due to the sudden and unexpected character of this phenomenon. The effects of seismic events on the surface, i.e. ground deformation had been difficult to measure with traditional geodetic methods, which are based on discrete point observations and are carried out at temporal intervals and in fixed locations (e.g. levelling lines). Development of radar remote sensing (InSAR) techniques and proliferation of open satellite radar data such as Sentinel- 1 mission provides means that can now be successfully applied to investigate areas and ground movements affected by seismicity induced by mining. In this paper four induced seismic events with magnitudes from 4.5 to 4.8 that occurred between 16 December 2016 and 15 September 2018 in the Rudna underground copper mine area in SW Poland have been investigated with differential satellite radar interferometry (DInSAR). Based on the results of processing of 37 pairs of Sentinel-1 data captured before and after each of these events, deformation areas have been spatially localised and vertical displacement and extent of deformation have been calculated. The mean maximum vertical displacements range from −70 mm for the 4.5 magnitude tremor to −94 mm for the 4.8 magnitude event. Whereas, mean extent ranges from 1.5 km to 1.9 km in the W-E direction and from 1.8 km to 2.1 km in the N-S direction. A linear relation between magnitude of induced tremor and increase in vertical displacement and extent of the ground deformation has been established. Moreover, the results of this study indicate that InSAR is adequately accurate technique to analyse ground displacements caused by mining induced tremors and provides continuous field data on the geometry of the resulting deformation areas.
ESTIMATING 3D LAND SUBSIDENCE FROM MULTI-TEMPORAL SAR IMAGES AND GNSS DATA USING WEIGHTED LEAST SQUARES
Authors: Susaki J. , Kusakabe T. , Anahara T.
Published: August 2020

Abstract. Analysis of multi-temporal synthetic aperture radar (SAR) satellite images using persistent scatterer interferometry is an effective approach for monitoring land subsidence, which is a serious issue in some urban areas. However, a drawback to this approach is that it is limited to displacement along the radar line-of-sight direction. An accurate understanding of land subsidence requires estimation of 3D displacement. One solution is to combine observations from multiple sources and directions, such as multi-temporal SAR images acquired on ascending and descending orbits, with global navigation satellite system (GNSS) data. While this approach estimates 3D displacement, other methods do not account for differences in data accuracy. Therefore, in this paper, we propose a method for estimating 3D land subsidence from multi-temporal SAR images and GNSS data by using the weighted least squares method. The weights for data sources are calculated from the PSI results and GNSS data. We apply the method to Kansai International Airport, using 13 ALOS-2/PALSAR-2 ascending images from 2014 to 2018 and 17 ALOS-2/PALSAR-2 descending images from 2015 to 2018. Root mean squared errors in the east–west, north–south and vertical directions are 6, 13, and 10 mm/year, respectively. These results demonstrate that combining PSI and geodetic results is effective for monitoring land deformation accurately with high spatial resolution.
INCORPORATING INDEPENDENT COMPONENT ANALYSIS AND MULTI-TEMPORAL SAR TECHNIQUES TO RETRIEVE RAPID POSTSEISMIC DEFORMATION
Authors: Vajedian S.
Published: August 2020

Abstract. This study investigates the ongoing postseismic deformation induced by two moderate mainshocks of Mw 6.1 and Mw 6.0, 2017 Hojedk earthquake in Southern Iran. Available Sentinel-1 TOPS C-band Synthetic Aperture Radar (SAR) images over about one year after the earthquakes are used to analyze the postseismic activities. An adaptive method incorporating Independent Component Analysis (ICA) and multi-temporal Small BAseline Subset (SBAS) Interferometric SAR (InSAR) techniques is proposed and implemented to recover the rapid deformation. This method is applied to the series of interferograms generated in a fully constructed SBAS network to retrieve the postseismic deformation signal. ICA algorithm uses a linear transformation to decompose the input mixed signal to its source components, which are non-Gaussian and mutually independent. This analysis allows extracting the low rate postseismic deformation signal from a mixture of interferometric phase components. The independent sources recovered from the multi-temporal InSAR dataset are then analyzed using a group clustering test aiming to identify and enhance the undescribed deformation signal. Analysis of the processed interferograms indicates a promising performance of the proposed method in determining tectonic deformation. The proposed method works well, mainly when the tectonic signal is dominated by the undesired signals, including atmosphere or orbital/unwrapping noise that counts as temporally uncorrelated components.In contrast to the standard SBAS time series method, the ICA-based time series analysis estimates the cumulative deformation with no prior assumption about elevation dependence of the interferometric phase or temporal nature of the tectonic signal. Application of the method to 433 Sentinel-1 pairs within the dataset reports two distinct deformation patches corresponding to the postseismic deformation. Besides the performance of the ICA-based analysis, the proposed method automatically detects rapid or low rate tectonic processes in unfavorable conditions.
NEW SOLAR-INDUCED CHLOROPHYLL FLUORESCENCE RETRIEVAL ALGORITHM BASED ON TANSAT SATELLITE DATA
Authors: Zhou Y. , Lu X. , Huang Y. , Gao Z. , Zheng Y.
Published: August 2020

Abstract. Solar-induced chlorophyll fluorescence (SIF) is an indicator of plant photosynthesis which could be detected by satellite. However，some existing algorithms are easily affected by the inaccuracy of satellite data which will causing deviation in the retrieval of SIF. To avoid "outliers" with inaccuracy affecting the retrieval results, a random sample consensus algorithm (RANSAC) was introduced to retrieve SIF in this paper. The results show that the chlorophyll fluorescence value obtained by this method is consistent with the OCO-2 SIF product (R2 = 0.81), and also consistent with the MODIS vegetation index (R2 = 0.87 with NDVI, R2 = 0.85 with EVI). Compared with the existing SIF products (OCO-2 SIF), the SIF retrieved in this paper was better in spatial details and outlier distribution.
ENHANCEMENT OF DEPTH MAP BY FUSION USING ADAPTIVE AND SEMANTIC-GUIDED SPATIOTEMPORAL FILTERING
Authors: Albanwan H. , Qin R.
Published: August 2020

Abstract. Extracting detailed geometric information about a scene relies on the quality of the depth maps (e.g. Digital Elevation Surfaces, DSM) to enhance the performance of 3D model reconstruction. Elevation information from LiDAR is often expensive and hard to obtain. The most common approach to generate depth maps is through multi-view stereo (MVS) methods (e.g. dense stereo image matching). The quality of single depth maps, however, is often prone to noise, outliers, and missing data points due to the quality of the acquired image pairs. A reference multi-view image pair must be noise-free and clear to ensure high-quality depth maps. To avoid such a problem, current researches are headed toward fusing multiple depth maps to recover the shortcomings of single-depth maps resulted from a single pair of multi-view images. Several approaches tackled this problem by merging and fusing depth maps, using probabilistic and deterministic methods, but few discussed how these fused depth maps can be refined through adaptive spatiotemporal analysis algorithms (e.g. spatiotemporal filters). The motivation is to push towards preserving the high precision and detail level of depth maps while optimizing the performance, robustness, and efficiency of the algorithm.
A CAUSAL HIERARCHICAL MARKOV FRAMEWORK FOR THE CLASSIFICATION OF MULTIRESOLUTION AND MULTISENSOR REMOTE SENSING IMAGES
Authors: Montaldo A. , Fronda L. , Hedhli I. , Moser G. , Serpico S. B. , Zerubia J.
Published: August 2020

Abstract. In this paper, a multiscale Markov framework is proposed in order to address the problem of the classification of multiresolution and multisensor remotely sensed data. The proposed framework makes use of a quadtree to model the interactions across different spatial resolutions and a Markov model with respect to a generic total order relation to deal with contextual information at each scale in order to favor applicability to very high resolution imagery. The methodological properties of the proposed hierarchical framework are investigated. Firstly, we prove the causality of the overall proposed model, a particularly advantageous property in terms of computational cost of the inference. Secondly, we prove the expression of the marginal posterior mode criterion for inference on the proposed framework. Within this framework, a specific algorithm is formulated by defining, within each layer of the quadtree, a Markov chain model with respect to a pixel scan that combines both a zig-zag trajectory and a Hilbert space-filling curve. Data collected by distinct sensors at the same spatial resolution are fused through gradient boosted regression trees. The developed algorithm was experimentally validated with two very high resolution datasets including multispectral, panchromatic and radar satellite images. The experimental results confirm the effectiveness of the proposed algorithm as compared to previous techniques based on alternate approaches to multiresolution fusion.
WETLAND MAPPING IN NEW BRUNSWICK, CANADA WITH LANDSAT5-TM, ALOS-PALSAR, AND RADARSAT-2 IMAGERY
Authors: LaRocque A. , Leblon B. , Woodward R. , Bourgeau-Chavez L.
Published: August 2020

Abstract. Several maps of wetland areas in central New Brunswick, Canada, were produced by applying the Random Forests classifier to different combinations of optical Landsat-5 TM images, dual-polarized (HH, HV) Radarsat-2 C-band and Alos-1 PalSAR L-band Synthetic Aperture Radar (SAR) images and digital elevation data. The resulting maps were compared to 199 GPS wetland sites that were visited between 2012 and 2018 as well as to a combination of two wetland maps currently used by the Province of New Brunswick. The number of correctly identified GPS wetland sites was the highest when both the Alos-PalSAR and Radarsat-2 images are used (97.9%). This percentage of correctly identified sites were well above the accuracy of the official New Brunswick wetland maps (44.7 %). With the best-classified image, the misidentifications were due to wetlands not being classified in the right wetland class, and just one case was a wetland site being classified in a non-wetland class. For the NB wetland map, about a quarter of the wetland validation sites were classified in a non-wetland class, and about the same number of sites were classified in the wrong wetland class.
A BOTTOM-UP QUANTITATIVE MECHANISM FOR UPDATING THE SELF-MODIFICATION PARAMETERS IN THE SLEUTH MODEL
Authors: Liu F. , Sun W. L.
Published: August 2020

Abstract. Generally, the default Self-Modification Parameters (SMPs) values, rather than the proper SMPs parameters group, has been applied in the SLEUTH model. However, various pre-setting of SMPs will simulate different morphology and structure of urban sprawl. The study is intended to propose a practical tool for the quantification response to model input variables on modelling complex urban systems. In this research, the parameter weight sorting job has been carried out to provide an adjusting priority experience. Besides, the model output imagery indices were used to describe the morphology, distribution of the urban prediction sprawl and the correlationship with urban road network effectively. Finally, the adjusting factors have been calculated with AHP method. The work helps geographers to determine how to make further use of the inner forward transmission mechanism on the SLEUTH model for further improving it in performance.
DEEP LEARNING BASED OPTICAL FLOW ESTIMATION FOR CHANGE DETECTION: A CASE STUDY IN INDONESIA EARTHQUAKE
Authors: Qiao H. J. , Wan X. , Xu J. Z. , Li S. Y. , He P. P.
Published: August 2020

Abstract. Real-time change detection and analysis of natural disasters is of great importance to emergency response and disaster rescue. Recently, a number of video satellites that can record the whole process of natural disasters have been launched. These satellites capture high resolution video image sequences and provide researchers with a large number of image frames, which allows for the implementation of a rapid disaster procedure change detection approach based on deep learning. In this paper, pixel change in image sequences is estimated by optical flow based on FlowNet 2.0 for quick change detection in natural disasters. Experiments are carried out by using image frames from Digital Globe WorldView in Indonesia Earthquake took place on Sept. 28, 2018. In order to test the efficiency of FlowNet 2.0 on natural disaster dataset, 7 state-of-the-art optical flow estimation methods are compared. The experimental results show that FlowNet 2.0 is not only robust to large displacements but small displacements in natural disaster dataset. Two evaluation indicators: Root Mean Square Error (RMSE) and Mean Value are used to record the accuracy. For estimation error of RMSE, FlowNet 2.0 achieves 0.30 and 0.11 pixels in horizontal and vertical direction, respectively. The error in horizontal error is similar to other algorithms but the value in vertical direction is significantly lower than them. And the Mean Value are 1.50 and 0.09 pixels in horizontal and vertical direction, which are most close to the ground truth comparing to other algorithms. Combining the superiority of computing time, the paper proves that only the approach based on FlowNet 2.0 is able to achieve real-time change detection with higher accuracy in the case of natural disasters.
AUTOMATIC BENGGANG RECOGNITION BASED ON LATENT SEMANTIC FUSION OF UHR DOM AND DSM FEATURES
Authors: Shen S. , Zhang T. , Zhao Y. , Wang Z. , Qian F.
Published: August 2020

Abstract. Benggang are characterized by deep-cut slopes with various shapes and depressions on the vast weathered crust slopes in southern China. The gully heads have been continuously collapsed and eroded to form a chair-like erosion landforms. It develops rapidly, and leads to large amounts of erosion, with the hazards of damaging land resources, destroying basic farmland, and deteriorating ecological environment. To study and manage Benggang, the primary task is to discover it. Traditional methods based on local in-situ investigations, which are not only labour-consuming but also inefficient. These methods are difficult to meet the needs of large-scale investigations of Benggang. This paper proposes a method for automatic Benggang recognition based on Ultra-High Resolution (UHR) DOM (Digital Orthophoto Map) and DSM (Digital Surface Model) obtained from UAV (Unmanned Aerial Vehicle) survey. This method adopts a Bag of Visual-Topographical Words (BoV-TW) model. The local features extracted from DOM and DSM are represented based on BoV-TW, and fused by Latent Dirichlet Allocation (LDA). Finally Support Vector Machine (SVM) is adopted as a supervised classifier to achieve high-precision automatic Benggang recognition. Experimental results prove that the total accuracy of our method can be maintained at about 95%, with recall and precision above 80% (the highest are 97.22% and 94.44%, respectively), which are significantly higher than the methods of using only DOM local features and using only BoV-TW.
INVESTIGATIONS ON SKIP-CONNECTIONS WITH AN ADDITIONAL COSINE SIMILARITY LOSS FOR LAND COVER CLASSIFICATION
Authors: Yang C. , Rottensteiner F. , Heipke C.
Published: August 2020

Abstract. Pixel-based land cover classification of aerial images is a standard task in remote sensing, whose goal is to identify the physical material of the earth’s surface. Recently, most of the well-performing methods rely on encoder-decoder structure based convolutional neural networks (CNN). In the encoder part, many successive convolution and pooling operations are applied to obtain features at a lower spatial resolution, and in the decoder part these features are up-sampled gradually and layer by layer, in order to make predictions in the original spatial resolution. However, the loss of spatial resolution caused by pooling affects the final classification performance negatively, which is compensated by skip-connections between corresponding features in the encoder and the decoder. The most popular ways to combine features are element-wise addition of feature maps and 1x1 convolution. In this work, we investigate skip-connections. We argue that not every skip-connections are equally important. Therefore, we conducted experiments designed to find out which skip-connections are important. Moreover, we propose a new cosine similarity loss function to utilize the relationship of the features of the pixels belonging to the same category inside one mini-batch, i.e. these features should be close in feature space. Our experiments show that the new cosine similarity loss does help the classification. We evaluated our methods using the Vaihingen and Potsdam dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 91.1% for both test sites.
IDENTIFYING THE DRIVING FACTORS OF POPULATION EXPOSURE TO FINE PARTICULATE MATTER (PM2.5) IN WUHAN, CHINA
Authors: Yang C. , Zhan Q. , Xiao Y. , Liu H.
Published: August 2020

Abstract. Characterizing the spatiotemporal dynamics of population exposure to fine particulate matter (PM2.5) and the underlying external forcing can provide proactive implication for public health precautions. In this study, satellite-derived surface-level PM2.5 concentration as well as landscape factors and socioeconomic data are collected to identify the inter-annual variations and potential driving forces of population exposure to fine particulate matter (PM2.5) in Wuhan, China from 2000 to 2015. The fine-scale PM2.5 exposures in 2000, 2005, 2010 and 2015 were first estimated. Then the contributions of landscape factors and socioeconomic forcing are quantified by a machine learning method (i.e. Random Forest). The results revealed that the population in Wuhan faced increasing and more clustering PM2.5 threats from 2000 to 2010. Then a weakened and dispersed health threat of PM2.5 was witnessed in 2015. In general, the Gross Domestic Product (GDP) contributed the most to high-level PM2.5 exposure in the period of 2000–2015, i.e. variable importance (VIM) equalled to xxx. Among all the biophysical and landscape characteristics, the percentage of urban landscape (PLAND_UA) and urban area fraction were attributed the most to the PM2.5 population exposure. In parallel, precipitation played a crucial part in the mitigation of PM2.5 exposure. The identification of inter-annual dynamics of population PM2.5 exposure and the underlying forcing can facilitate the decision making and epidemiological precautions in the evaluation and alleviation of population exposure risks.
DETAILED VALIDATION OF AMSR2 SEA ICE CONCENTRATION DATA USING MODIS DATA IN THE SEA OF OKHOTSK
Authors: Cho K. , Naoki K. , Comiso J.
Published: August 2020

Abstract. Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scanning Radiometer AMSR2 which was launched by JAXA in May 2012 on-board GCOM-W satellite provides brightness temperature data that are used to estimate sea ice concentration, the fundamental parameter that is used to monitor the sea ice cover. JAXA is providing AMSR2 sea ice concentration data, derived using ASMR2 Bootstrap Algorithm as a standard product of AMSR2, as a means to communicate how the sea ice cover is changing. This paper describes the advantages of AMSR2 in calculating sea ice concentration and evaluate the accuracy of the sea ice concentration in the Sea of Okhotsk by comparing the result with simultaneously collected MODIS data. The result suggested that under normal winter condition, the RMSE of the AMSR2 sea ice concentration could be less than 10%.
USING TIME SERIES OF SENTINEL-1 IMAGES TO PRODUCE DRY BATHYMETRY OF RIVERS
Authors: Maillard P.
Published: August 2020

Abstract. A method is presented to produce river cross section profiles from a time series of Sentinel-1 images paired with water level data. Four programs are presented that generate river width data and cross section profiles from SAR or optical images. The programs generate a river bank and island width database with minimum manual intervention. Water level data from in situ stations are interpolated to match the width data and create elevation points from which the cross section profiles are produced. The method is fully described and tested on the São Francisco River in Brazil. The width data are plotted against discharge data to compare their progression. Over 1700 cross sections were produced and classified by their shape. Potential and limitations are presented.
THE REVIEW OF SOIL MOISTURE MULTI-SCALE VERIFICATION METHODS
Authors: Pang Z. , Qin X. , Jiang W. , Fu J. , Yang K. , Lu J. , Qu W. , Li L. , Li X.
Published: August 2020

Abstract. Soil moisture is an important physical parameter to investigate water circulation, while it is difficult to be measured with spatiotemporal consistency. During the past several decades, a larger number of soil moisture verification methods were proposed, however, the review of soil moisture verification method from multi-scale perspective is still lacking. This paper investigates the verification method of soil moisture from three scale, such as point-scale, regional scale and remote sensing data verification. The prospect of soil moisture verification is proposed to serve retrieval algorithm validation.
CHESTNUT COVER AUTOMATIC CLASSIFICATION THROUGH LIDAR AND SENTINEL-2 MULTI-TEMPORAL DATA
Authors: Alonso L. , Armesto J. , Picos J.
Published: August 2020

Abstract. Chestnut (Castanea sativa Mill.) managed forests in Galicia (Northwestern Spain) have important cultural, economic and ecosystem values. However, due to rural exodus chestnut stands are being degraded. In order to take restoration and conservation measures knowledge of these forests' location, expanse and stage is needed. The available Spanish official cartography is based on photointerpretation which is inaccurate in terms of chestnut forest location and classification. However, remote sensing has recently been proven to be an effective tool for this purpose. Sentinel 2 multi-temporal classification is recently acquiring importance as a method to classify tree species. This project intends to detect chestnut forests using LiDAR and Sentinel 2 multi-temporal data and to compare these results with those obtained using the official cartography. It also intends to assess how the use of different phenological stages could improve classification results. The results obtained provide an overall accuracy of 76% when a three-month combination is used: (March, July and September) leaf-off stage, flowering and leaf-on stage. Overlapping of the current map and the official cartography lead to an accuracy and precision increase; highlighting the utility of the presented methodology to acquire knowledge about chestnut forests location.
ADDRESSING THE CLASS IMBALANCE PROBLEM IN THE AUTOMATIC IMAGE CLASSIFICATION OF COASTAL LITTER FROM ORTHOPHOTOS DERIVED FROM UAS IMAGERY
Authors: Duarte D. , Andriolo U. , Gonçalves G.
Published: August 2020

Abstract. Unmanned Aerial Systems (UAS) has been recently used for mapping marine litter on beach-dune environment. Machine learning algorithms have been applied on UAS-derived images and orthophotos for automated marine litter items detection. As sand and vegetation are much predominant on the orthophoto, marine litter items constitute a small set of data, thus a class much less represented on the image scene. This communication aims to analyse the class imbalance issue on orthophotos for automated marine litter items detection. In the used dataset, the percentage of patches containing marine litter is close to 1% of the total amount of patches, hence representing a clear class imbalance issue. This problem has been previously indicated as detrimental for machine learning frameworks. Three different approaches were tested to address this imbalance, namely class weighting, oversampling and classifier thresholding. Oversampling had the best performance with a f1-score of 0.68, while the other methods had f1-score value of 0.56 on average. The results indicate that future works devoted to UAS-based automated marine litter detection should take in consideration the use of the oversampling method, which helped to improve the results of about 7% in the specific case shown in this paper.
OPTIMAL DATES FOR DECIDUOUS TREE SPECIES MAPPING USING FULL YEARS SENTINEL-2 TIME SERIES IN SOUTH WEST FRANCE
Authors: Karasiak N. , Fauvel M. , Dejoux J.-F. , Monteil C. , Sheeren D.
Published: August 2020

Abstract. The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1 = 0.99, F1 = 0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1 = 0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.
REMOTE SENSING MONITORING AND ANALYSIS OF AGRICULTURAL DROUGHT IN SPRING CONSIDERING IRRIGATION AREA: A CASE STUDY OF DONGLEI IRRIGATION DISTRICT (PHASE II)
Authors: Liu H. , Song W. , Duan Y. , Sun T. , Li H. , Tian L. , Lu Y. , Liu Y. , Li M.
Published: August 2020

Abstract. As one of the common natural disasters, drought is extremely destructive to agriculture. Drought has risen in recent years, so how to effectively monitor drought has become the focus of attention of relevant government departments. When drought occurs, irrigation measures will be taken in the irrigation district for drought resistance and disaster reduction. At this time, drought monitoring based on meteorological data and remote sensing index has certain limitations. Based on the surface temperature data of MODIS, this paper extracted the irrigation area information in the irrigated district, compared the drought monitoring results of irrigated area with those based on precipitation anomaly percentage and vegetation anomaly index, and analysed the importance of agricultural drought monitoring based on remote sensing of irrigated area. This paper considers that it is necessary to consider the distribution of the irrigated area when conducting drought monitoring in the irrigation district, it can improve the accuracy of agricultural monitoring, and it can provide the basis for government and agriculture and other related departments to manage water resources and formulate drought-resistance policies.
SUGARCANE PLANTATION MAPPING USING DYNAMIC TIME WARPING FROM MULTI-TEMPORAL SENTINEL-1A RADAR IMAGES
Authors: Olfindo Jr. N. T. , de la Cruz R. M. , Borlongan N. J. B. , Marciano Jr. J. J. S. , Olalia L. C.
Published: August 2020

Abstract. Updating of seasonal agricultural crop map is limited by the local knowledge of the mapper. Mapping of previously unaccounted agricultural plots involve massive field works aided by very high-resolution images. The phenological cycle of seasonal crops like sugarcane, with a range of ten (10) to twelve (12) months from planting to harvesting, exhibit a unique characteristic in terms of radar backscatter and time. In this paper, a pattern matching algorithm was tested to detect sugarcane plantations. Dynamic Time Warping (DTW), which was originally used for voice recognition, was used to detect sugarcane plantations from multitemporal Sentinel-1A images. Using known sugarcane plots, temporal signatures were gathered and used to detect other plantations in the area. The result helped the Sugar Regulatory Administration (SRA) in updating the inventory of sugarcane plantations faster with detection accuracy of more than 92 percent.
ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA
Authors: Pittman R. , Hu B.
Published: August 2020

Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.
REVEALING LONG-TERM PHYSIOLOGICAL TRAJECTORIES OF GRASSLANDS FROM LEGACY B&W AERIAL PHOTOGRAPHS
Authors: Poterek Q. , Herrault P.-A. , Forestier G. , Schwartz D.
Published: August 2020

Abstract. Landscape reconstruction is crucial to measure the effects of climate change or past land use on current biodiversity. In particular, retracing past phenological changes can serve as a basis for explaining current patterns of plant communities and predict the future extinction of species. Old spatial data are currently used to reconstruct vegetation changes, both morphologically (with landscape metrics) and semantically (grasslands to crops for instance). However, poor radiometric properties (single panchromatic channel, illumination variation, etc.) do not offer the possibility to compute environmental variables (e.g. NDVI and color indices), which strongly limits long-term phenological reconstruction. In this study, we propose a workflow for reconstructing phenological trajectories of grasslands from 1958 to 2011, in the French central Vosges, from old aerial black and white (B&W) photographs. Noise and vignetting corruptions were first corrected in B&W photographs with non-local filtering algorithms. Panchromatic scans were then colorized with a Generative Adversarial Network (GAN). Based on the predicted channels, we finally computed digital greenness metrics (Green Chromatic Coordinate, Excess Greenness) to measure vegetation activity in grasslands. Our results demonstrated the feasibility of reconstructing long-term phenological trajectories from legacy photographs with insights at different levels: (1) the proposed correction methods provided radiometric improvements in old aerial missions; (2) the colorization process led to promising and plausible colorized historical products; (3) digital greenness metrics were useful for describing past vegetation activity.
A GLOBAL SHAPE MODEL FOR SATURN’S MOON ENCELADUS FROM A DENSE PHOTOGRAMMETRIC CONTROL NETWORK
Authors: Bland M. T. , Weller L. A. , Mayer D. P. , Archinal B. A.
Published: August 2020

Abstract. A planetary body’s global shape provides both insight into its geologic evolution, and a key element of any Planetary Spatial Data Infrastructure (PSDI). NASA’s Cassini mission to Saturn acquired more than 600 moderate- to high-resolution images (<500 m/pixel) of the small, geologically active moon Enceladus. The moon’s internal global ocean and intriguing geology mark it as a candidate for future exploration and motivates the development of a PSDI. Recently, two PSDI foundational data sets were created: geodetic control and orthoimages. To provide the third foundational data set, we generate a new shape model for Enceladus from Cassini images and a dense photogrammetric control network (nearly 1 million tie points) using the U.S. Geological Survey’s Integrated Software for Imagers and Spectrometers (ISIS) and the Ames Stereo Pipeline (ASP). The new shape model is near-global in extent and gridded to 2.2 km/pixel, ∼50 times better resolution than previous global models. Our calculated triaxial shape, rotation rate, and pole orientation for Enceladus is consistent with current International Astronomical Union (IAU) values to within the error; however, we determined a new prime meridian offset (Wo) of 7.063°. We calculate Enceladus’ long-wavelength topography by subtracting the best-fit triaxial ellipsoid from our shape model. The result is comparable to previous global models but can resolve topographic features as small as 5–7 km across in certain areas. To evaluate the spatially varying quality of the model, we calculate the point density (variable from 5 to more than 50 points per pixel), normalized median absolute deviation of the points within each pixel (typically less than 100 m), and the minimum expected vertical precision of each point (ranging from 29 m to 2 km).
A SIMPLE AND EFFICIENT CROSS-SENSOR RETRIEVAL METHOD FOR RETRIEVING STEREO IMAGES BY MULTISPECTRAL IMAGE
Authors: Peng F. , Lu W. , Xu L. , Cai X.
Published: August 2020

Abstract. Some users need to utilize a query multispectral image to quickly locate desired panchromatic stereo images from massive remotely sensed images. A stereo pair or triplet is different with a multispectral image in terms of the viewing number, viewing angle, band, radiometric resolution, spatial resolution, and ability to obtain height information. To perform the cross-sensor retrieval, the orthoimage or digital surface model (DSM) is usually produced from stereo images in a long time, drastically reducing the retrieval efficiency. To achieve a high efficiency, our study explores the potential of the raw viewing images of stereo images to be immediately used in the retrieval. We proposed a simple and efficient cross-sensor retrieval method by doing similarity matching between the query multispectral image and the raw viewing images of stereo images, using probability histograms separately produced from them. Experimental results show that our method outperformed two methods based on the orthoimage in terms of both the retrieval efficiency and precision. Our method handily deals with differences between stereo images and multispectral images, and efficiently achieves the high-accuracy cross-sensor retrieval with no need to produce the orthoimage or DSM.