bluuebunny/crossref_metadata_2025 huggingface dataset:
RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES
Authors: Hessel C. , Grompone von Gioi R. , Morel J. M. , Facciolo G. , Arias P. , de Franchis C.
Published: August 2020

Abstract. We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen by maximization of a quality metric. For each image, two affine correction models are robustly estimated using random sample consensus, using the two closest reference images; the final correction is obtained by linear interpolation. For each pair of source and reference images, pseudo-invariant features are obtained using a similarity measure invariant to radiometric changes. A final tone-mapping step outputs the images in the standard 8-bits range. This method is illustrated by the fusion of time series of Sentinel-2 at correction levels 1C, 2A, and Landsat-8 images. By using only the atmospherically corrected Sentinel-2 L2A images as anchors, the full output series inherits this atmospheric correction.
CLOUD DETECTION FOR NIGHT-TIME PANCHROMATIC VISIBLE AND NEAR-INFRARED SATELLITE IMAGERY
Authors: Joachim L. , Storch T.
Published: August 2020

Abstract. Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites.
STUDY OF LARGE-SCALE BLOCK ADJUSTMENT WITH LASER ALTIMETRY DATA GLAS OF ZY3 IMAGES OVER SHANDONG
Authors: Wang T. , Li X.
Published: August 2020

Abstract. In the global mapping work, there will be no control points or uneven distribution of control points. The traditional method of single scene image correction based on ground control points is not suitable for modern surveying and mapping, which has no available control points in a large range. New control data is urgently needed to make up for the influence of traditional control points on surveying and mapping. In this paper, aiming at improving the elevation accuracy of ZY-3 uncontrolled point surveying, a filtering principle based on the analysis based on waveform analysis and the stereo image pair based on tie points is proposed for the screening of GLAS laser elevation points. A new method using high-reliability laser elevation control points as the height constraint and the regional network adjustment of ZY-3 images without control points is studied. Through the experiment in Shandong Province, it is proved that the elevation accuracy of the adjustment can be improved obviously by introducing the selected laser points. The mean error of adjustment elevation is increased from −2.297 to 0.216m and the mean square error of elevation is increased from 3.193m to 2.007m, significantly improving the elevation positioning accuracy of adjustment.
DISIR: DEEP IMAGE SEGMENTATION WITH INTERACTIVE REFINEMENT
Authors: Lenczner G. , Le Saux B. , Luminari N. , Chan-Hon-Tong A. , Le Besnerais G.
Published: August 2020

Abstract. This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. Importantly, user annotations modify the inputs of the network – not its weights – enabling a fast and smooth process. Through experiments on two public aerial datasets, we show that user annotations are extremely rewarding: each click corrects roughly 5000 pixels. We analyze the impact of different aspects of our framework such as the representation of the annotations, the volume of training data or the network architecture. Code is available at this address.
RESOLUTION-AWARE NETWORK WITH ATTENTION MECHANISMS FOR REMOTE SENSING OBJECT DETECTION
Authors: Tian Z. , Wang W. , Tian B. , Zhan R. , Zhang J.
Published: August 2020

Abstract. Nowadays, deep-learning-based object detection methods are more and more broadly applied to the interpretation of optical remote sensing image. Although these methods can obtain promising results in general conditions, the designed networks usually ignore the characteristics of remote sensing images, such as large image resolution and uneven distribution of object location. In this paper, an effective detection method based on the convolutional neural network is proposed. First, in order to make the designed network more suitable for the image resolution, EfficientNet is incorporated into the detection framework as the backbone network. EfficientNet employs the compound scaling method to adjust the depth and width of the network, thereby meeting the needs of different resolutions of input images. Then, the attention mechanism is introduced into the proposed method to improve the extracted feature maps. The attention mechanism makes the network more focused on the object areas while reducing the influence of the background areas, so as to reduce the influence of uneven distribution. Comprehensive evaluations on a public object detection dataset demonstrate the effectiveness of the proposed method.
LOW-RANK MATRIX DECOMPOSITION WITH SUPERPIXEL-BASED STRUCTURED SPARSE REGULARIZATION FOR MOVING OBJECT DETECTION IN SATELLITE VIDEOS
Authors: Zhang J. , Jia X. , Hu J.
Published: August 2020

Abstract. With new accessibility to satellite videos, retrieving the dynamic information of moving objects over a vast territory becomes possible with the development of advanced video processing and machine learning techniques. Detecting moving objects can be based on the structures of both background and foreground of a satellite video, and the background is assumed to lay in a low dimensional subspace. As the moving objects in satellite videos are groups of neighbouring pixels other than isolated pixels, Low-rank and Structured Sparse Decomposition (LSD) with structured sparsity regularization on the foreground can suppress the false alarms caused by isolated outliers. However, in LSD, the groups of neighbouring pixels are extracted by a fixed sliding window over each video frame, which ignores the coherence on the appearance of a moving object. For example, a moving object can be in an irregular shape and arbitrary orientation. In this paper, we argue that the spatial groups on the foreground can be defined using the concept of superpixels, where each superpixel is formed by a group of spatially connected similar pixels obtained from over-segmentation. We conduct low-rank matrix decomposition at superpixel level, which is named as Superpixel-based LSD (S-LSD). To handle the variation in moving objects, we combine the superpixels at a range of scales in the superpixel-based spatial regularization on the foreground. With the reduction in the number of spatial groups, S-LSD presents reduced computation complexity. The results on two satellite videos show a satisfactory performance with a significant saving in processing time when the proposed S-LSD approach is applied.
AUTOMATIC CLUSTERING OF CELTIC COINS BASED ON 3D POINT CLOUD PATTERN ANALYSIS
Authors: Horache S. , Goulette F. , Deschaud J.-E. , Lejars T. , Gruel K.
Published: August 2020

Abstract. The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins. It is based on three steps: registration, comparison and graph-based clustering. Experimental results on 90 coins coming from a Celtic treasury from the II-Ith century BC show a clustering quality equivalent to expert’s work.
FUSED 3D TRANSPARENT VISUALIZATION FOR LARGE-SCALE CULTURAL HERITAGE USING DEEP LEARNING-BASED MONOCULAR RECONSTRUCTION
Authors: Pan J. , Li L. , Yamaguchi H. , Hasegawa K. , Thufail F. I. ,   Brahmantara, Tanaka S.
Published: August 2020

Abstract. This paper proposes a fused 3D transparent visualization method with the aim of achieving see-through imaging of large-scale cultural heritage by combining photogrammetry point cloud data and 3D reconstructed models. 3D reconstructed models are efficiently reconstructed from a single monocular photo using deep learning. It is demonstrated that the proposed method can be widely applied, particularly to instances of incomplete cultural heritages. In this study, the proposed method is applied to a typical example, the Borobudur temple in Indonesia. The Borobudur temple possesses the most complete collection of Buddhist reliefs. However, some parts of the Borobudur reliefs have been hidden by stone walls and became not visible following the reinforcements during the Dutch rule. Today, only gray-scale monocular photos of those hidden parts are displayed in the Borobudur Museum. In this paper, the visible parts of the temple are first digitized into point cloud data by photogrammetry scanning. For the hidden parts, a 3D reconstruction method based on deep learning is proposed to reconstruct the invisible parts into point cloud data directly from single monocular photos from the museum. The proposed 3D reconstruction method achieves 95% accuracy of the reconstructed point cloud on average. With the point cloud data of both the visible parts and the hidden parts, the proposed transparent visualization method called the stochastic point-based rendering is applied to achieve a fused 3D transparent visualization of the valuable temple.
DEVELOPMENT AND VALIDATION OF A NEW PASSIVE MICROWAVE BASED SOIL MOISTURE INDEX
Authors: Zeng J. , Chen K.-S. , Cui C. , Bi H.
Published: August 2020

Abstract. Knowledge on the spatial-temporal variation of soil moisture is essential to many hydrometeorology applications. In this study, we proposed a new soil moisture index (SMI) from passive microwave observations, aiming to capture the soil moisture variability. The new SMI is developed based on the underlying physical basis that vegetation and surface roughness exert similar effects on the variation of land surface emissivity and microwave polarization difference radio (MPDI), but they act in an opposite way compared with soil moisture. Hence, we can obtain the SMI value in a two-dimensional space by combining use of land surface emissivity and MPDI to isolate the contribution of soil moisture and that of vegetation and surface roughness. We calculated the SMI by using the L-band SMAP Level-3 datasets and validated it with five well calibrated and dense soil moisture networks and also compared it with SMAP and ESA CCI soil moisture products. The results show the SMI exhibits the highest R (0.87) and lowest RMSE (0.028 m3 m−3) value after removing the systematic bias by using the cumulative distribution function (CDF) matching technique among the satellite products during the whole study period, thus demonstrating its good capability of tracking the temporal variation of soil moisture and its potential usage in various hydrometeorology applications.
IMPROVED TOPOGRAPHIC MAPPING IN VEGETATED MOUNTAINOUS AREAS BY HIGH-RESOLUTION RADARGRAMMETRY-ASSISTED SAR INTERFEROMETRY
Authors: Dong Y. , Zhang L. , Liao M.
Published: August 2020

Abstract. In densely vegetated mountainous areas, the height measurement accuracy of SAR interferometry is contaminated by phase unwrapping errors and volume scattering effects of the vegetation. Aiming at this limitation of InSAR, we propose the high-resolution radargrammetry-assisted SAR interferometry method to achieve high-precision topographic mapping. Specifically speaking, we propose to apply our methods in the TerraSAR-X images with a repeat-pass InSAR pair and a stereo radargrammetric pair sharing the same master image which can avoid radarcoding the reference DEM and minimize the acquisition time discrepancy between the reference DEM and InSAR data. Compared with medium-resolution reference DEM, the high-resolution radargrammetric DEM can simulate the reference topographic phase more accurately which reduces the InSAR phase unwrapping errors. In addition to the removal of the reference topographic phase, we automatically select reliable points from radargrammetric DEM for calibrating the absolute InSAR phase offset. Finally, we fuse the radargrammetric DEM and InSAR DEM together with weights determined from theoretical height error to fill in data voids in InSAR DEM. The experimental results show that the DEM generated by radargrammetry-assisted SAR interferometry has better height accuracy than DEM generated by either single method.
MODELLING AND PREDICTION OF PRECIPITATION AND SOIL MOVEMENT BASED ON ADINSAR
Authors: Yang C. H. , Müterthies A.
Published: August 2020

Abstract. Evaluating soil movement related to precipitation is needed for geologic and hydrologic applications. In principle, the soil body swells and shrinks depending on soil type, precipitation rate, moisture content, and drainage rate. The precipitations are normally measured at weather stations. Measuring the soil movement by using ground-based sensors and hydrologic models across a large area is costly and time-consuming. Also the weather observations were not fully involved in modelling. A long-term monitoring using remote sensing is a cost-effective alternative. For this purpose, we developed a new approach in this study to model the transformation between precipitation and soil movement. The time-series soil movement over a large area is evaluated by ADInSAR at mm/yr level. As a result, the predictive model can compute the precipitation at a location from its ADInSAR-derived movement, and vice versa. Our test using Sentinel-1 images shows that the prediction accuracy for precipitation is 14 mm (mean error rate 12%) and it amounts 12 mm/yr for soil movement. The accuracies indicate that our modelling is relevant to the reality. We also discuss the influences of different parameters on the modelling. In the future, we will proceed with tests considering other areas of interest, time spans, and image sources. More target points will be analysed in detail. Last but not least, we will work on the applications related to geology and hydrology.
VOLUME MEASUREMENT OF SAND CARRIER USING UAV-BASED MAPPING
Authors: He H. , Xu X. , Chen T. , Lu P.
Published: August 2020

Abstract. To improve the operation efficiency and reduce the risks associated with exposing surveyors to danger during on-site operations, this paper presents a volume measurement approach of the sand carrier using unmanned aerial vehicle (UAV) imagery. Instead of the 3D surface interpolation of the sand carrier by total station measuring method or real-time kinematic global positioning system (RTK GPS) measuring method, the finely detailed surfaces of the sand carriers are reconstructed from dense point clouds derived by UAV-based mapping. Then, the volume of sand is calculated by the differential method, which multiplies the height difference between the UAV-derived 3D surfaces of the vessel and sand by the resolution of these surfaces. A total of 10 sand carriers are selected to test and evaluate the performance of the proposed approach, the absolute values of relative deviation between the volume obtained by UAV-based and the reference volume are approximately equal to 2%, which can be considered acceptable and satisfy the need of volume measurement of sand carriers. Compared with the manual field measurement and the laser scanning-based method, the proposed approach performs better in the accuracy of volume measurement or the reliability under the circumstance of a moving carrier. The overall results suggest that UAV-mapping can be used as an effective alternative to the commonly used method for volume measurement of the sand carrier.
EVALUATING A CONVOLUTIONAL NEURAL NETWORK FOR FEATURE EXTRACTION AND TREE SPECIES CLASSIFICATION USING UAV-HYPERSPECTRAL IMAGES
Authors: Sothe C. , la Rosa L. E. C. , de Almeida C. M. , Gonsamo A. , Schimalski M. B. , Castro J. D. B. , Feitosa R. Q. , Dalponte M. , Lima C. L. , Liesenberg V. , Miyoshi G. T. , Tommaselli A. M. G.
Published: August 2020

Abstract. The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced feature extraction and classification methods. Different from the traditional feature extraction methods, that highly depend on user’s knowledge, the convolutional neural network (CNN)-based method can automatically learn and extract the spatial-related features layer by layer. However, in order to capture significant features of the data, the CNN classifier requires a large number of training samples, which are hardly available when dealing with tree species in tropical forests. This study investigated the following topics concerning the classification of 14 tree species in a subtropical forest area of Southern Brazil: i) the performance of the CNN method associated with a previous step to increase and balance the sample set (data augmentation) for tree species classification as compared to the conventional machine learning methods support vector machine (SVM) and random forest (RF) using the original training data; ii) the performance of the SVM and RF classifiers when associated with a data augmentation step and spatial features extracted from a CNN. Results showed that the CNN classifier outperformed the conventional SVM and RF classifiers, reaching an overall accuracy (OA) of 84.37% and Kappa of 0.82. The SVM and RF had a poor accuracy with the original spectral bands (OA 62.67% and 59.24%) but presented an increase between 14% and 21% in OA when associated with a data augmentation and spatial features extracted from a CNN.
SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES
Authors: Kim M. , Cho K. , Kim H. , Kim Y.
Published: August 2020

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.
COMPONENT SUBSTITUTION NETWORK FOR PAN-SHARPENING VIA SEMI-SUPERVISED LEARNING
Authors: Liu C. , Zhang Y. , Ou Y.
Published: August 2020

Abstract. Pan-sharpening refers to the technology which fuses a low resolution multispectral image (MS) and a high resolution panchromatic (PAN) image into a high resolution multispectral image (HRMS). In this paper, we propose a Component Substitution Network (CSN) for pan-sharpening. By adding a feature exchange module (FEM) to the widely used encoder-decoder framework, we design a network following the general procedure of the traditional component substitution (CS) approaches. Encoder of the network decomposes the input image into spectral feature and structure feature. The FEM regroups the extracted features and combines the spectral feature of the MS image with the structure feature of the PAN image. The decoder is an inverse process of the encoder and reconstructs the image. The MS and the PAN image share the same encoder and decoder, which makes the network robust to spectral and spatial variations. To reduce the burden of data preparation and improve the performance on full-resolution data, the network is trained through semi-supervised learning with image patches at both reduced-resolution and full-resolution. Experiments performed on GeoEye-1 data verifies that the proposed network has achieved state-of-the-art performance, and the semi-supervised learning stategy further improves the performance on full-resolution data.
SOCIAL INFORMATION FUSED URBAN FUNCTIONAL ZONES CLASSIFICATION NETWORK
Authors: Lu W. , Tao C. , Ji Q. , Li H.
Published: August 2020

Abstract. Fast-changing cities need efficient management. Accurate classification of urban functional zone (UFZ) can provide important reference for cities management. Remote sensing imagery (RSI) is large scale, high resolution and fast update, which can provide massive data for UFZ extraction. However, UFZ are more concerned with social attributes such as industrial production and commercial activities, while images can only provide visual features, which is not enough for an elaborate UFZ classification. To solve this problem, in this paper, we combine RSI and point of interest (POI) data together for UFZ classification, and propose a Social Information Fused Urban Functional Zones Classification Network (SIF-Net). For RSI, we simply use a Xception CNNs network extract the visual information. For POI data, we first build a coarse heatmap for each type of POI (e.g. retail, apartment…), and then combine them as a POI tensor. Afterward, we use a channel attention module (CAM) based CNN model to fuse heatmaps from each type of POI, and then build a fine distribution of UFZ as the social information. Finally, we fuse the visual information extracted from RSI and social information extracted from POI by concatenating them. By fusing this two complementary information, our method makes up for the shortcomings of extracting UFZ based on RSI and general CNNs only. Compared with current state-of-the-art methods, experiments show that the proposed SIF-Net can significantly improve the UFZ classification result.
GENERATING ARTIFICIAL NEAR INFRARED SPECTRAL BAND FROM RGB IMAGE USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
Authors: Yuan X. , Tian J. , Reinartz P.
Published: August 2020

Abstract. Near infrared bands (NIR) provide rich information for many remote sensing applications. In addition to deriving useful indices to delineate water and vegetation, near infrared channels could also be used to facilitate image pre-processing. However, synthesizing bands from RGB spectrum is not an easy task. The inter-correlations between bands are not clearly identified in physical models. Generative adversarial networks (GAN) have been used in many tasks such as generating photorealistic images, monocular depth estimation and Digital Surface Model (DSM) refinement etc. Conditional GAN is different in that it observes some data as a condition. In this paper, we explore a cGAN network structure to generate a NIR spectral band that is conditioned on the input RGB image. We test different discriminators and loss functions, and evaluate results using various metrics. The best simulated NIR channel has a mean absolute error of around 5 percent in Sentinel-2 dataset. In addition, the simulated NIR image can correctly distinguish between various classes of landcover.
VEGA-SCIENCE SERVICES FOR MONITORING THE IMPACT OF TECHNOGENIC WASTE AND DUMPS SOURCES ON ENVIRONMENT
Authors: Savorskiy V. P. , Loupian E. A. , Panova O. Y. , Konstantinova A. M. , Ermakov D. M. , Balashov I. V.
Published: August 2020

Abstract. The impact of hazardous waste on people and the environment from agricultural, industrial, military and mining activities form one of the most significant global environmental challenges. These wastes often include heavy metals, acid precipitates (sulfur oxide solutions), hydrocarbons, and other organic chemicals. Environmental impact of such technogenic waste and dumps sources (TWDS) is widely reflected in vegetation cover changes, which can be easily detected by satellite instruments. But application of satellite data for TWDS monitoring requires appropriate information tools. At presented work, the authors conducted the development of the design and implementation of these tools within frames VEGA-Science services (http://sci-vega.ru/eng/) in order to provide analysis of longterm variability of the vegetation cover state caused by the influence of TWDS. The result of the work was included in automated system for remote monitoring of vegetation cover (SRMV) of VEGA-Science system.
VARIATIONS OF VEGETATION NET PRIMARY PRODUCTIVITY AND ITS RESPONSES TO CLIMATE CHANGE FROM 1982 TO 2015 IN MONGOLIA
Authors: Bayarsaikhan S. , Mandakh U. , Dorjsuren A. , Batsaikhan B. , Bao Y. , Adiya Z. , Myagmartseren P.
Published: August 2020

Abstract. Climate warming in Mongolia is relatively high, with extreme dry climate, and low precipitation, the input of green vegetation on the ecosystem functioning is relatively high. The impacts of climate change are critically affected to desertification, biodiversity loses, decreases of water sources, land degradation of rangeland in Mongolia. In order to better adapt to such changing climate, it is important to understand the long terms vegetation dynamics and its relation with precipitation. In this study, the third-generation GIMMS NDVI data of NOAA satellites and CASA model with metrological data have been used to estimate NPP between 1982 and 2015 throughout Mongolia. Results show that during 34 years mean NPP seems to have decreased greatly from semi-arid in the North to desert in the South across natural zone in Mongolia. The average NPP value was averaged at 166.1 g C/m2 and ranged between 19 and 724.85 g C/m2 for the terrain land. 60% of total NPP was relating to annual precipitation about R2 = 0.78 (p = 0.000). Total amount of NPP between 1982 and 2015 was estimated to be 0.32 Pg C/year and 0.29 Pg C/year in 1982 and 2015, respectively, with an average amount of NPP was 0.32 Pg (1 Pg = 1015 g) for 34 years. These results indicate that during most of vegetation growing season, NPP decreased by 0.03 Pg С/year. Field measurement data of 2007, 2009, 2014 and 2015 were used for correlation with the NPP estimation. As a result, R2 = 0.742 (p < 0.001) in 2007 for forest steppe, R2 = 0.74 (p < 0.001) in 2009 for meadow steppe and grassy steppe, R2 = 0.73 (p < 0.001) in 2014 for meadow steppe, R2 = 0.715 (p < 0.001) in 2015 for a desert steppe, respectively. The results obtained in this study contributes to understanding productivity of pasturelands of semi-arid ecosystems of Mongolia and Central Asia. By providing insights on the relationship between pasture productivity and climate variables such as precipitation and temperature, this study could be useful for national and regional scale climate change adaptation strategies.
ASSESSMENT OF EXPOSURE TO AMBIENT PM2.5 BASED ON GAP-FILLED AEROSOL OPTICAL DEPTH AT URBAN SCALE
Authors: Zhan Q. , Fan Z. , Xiao Y.
Published: August 2020

Abstract. Air pollution has been a crucial issue affecting human health and has drawn more and more attention in the world. The assessment of exposure to PM2.5 of urban residents based on remote sensing is challenging because of the data deficiency in aerosol optical depth (AOD) and the low spatial resolution. This article is devoted to adopt an approach with 2 gradient boosting decision tree (GBDT) models to fill the gaps in AOD and derive continuous PM2.5 distribution at urban scale. Then, the assessment of exposure to PM2.5 in Beijing was conducted. First, Simplified High Resolution MODIS Aerosol Retrieval Algorithm (SARA) was employed to obtain daily AOD from September 2016 to February 2017 at 500m resolution. Then we used the first GBDT to derive the gap-filled SARA AOD and the second GBDT to estimate PM2.5 spatial distribution based on multi-source data. Furthermore, population weighted exposure (PWE) levels of PM2.5 and population proportion exposed to PM2.5 concentration were estimated by PM2.5 distribution and population density data. The result demonstrates that both two GBDT models performed well with cross validation (CV) R2 of 0.86 and 0.85 on AOD gap-filling and PM2.5 concentration estimation respectively. The areas with high PM2.5 concentration are mainly distributed in the east and south of the city but the areas with higher PM2.5 exposure are mainly distributed in the urban centre. Moreover, it is found that over 80% people in Beijing are affected by PM2.5 pollution in autumn and winter. Overall, the approach this article applied and the analysis results are very useful for epidemiological investigation and air pollution control policy formulation.
GLACIER IDENTIFICATION FROM LANDSAT8 OLI IMAGERY USING DEEP U-NET
Authors: He Q. , Zhang Z. , Ma G. , Wu J.
Published: August 2020

Abstract. Glacier is one of the clearest signal of climate change, and its changes have important effects on regional climate and water resources. Glacier identification is the basic of glacial changes research. Traditional remote sensing glacier identification methods usually perform simple bands calculation based on the spectral characteristics of glacier. The identification results are greatly affected by threshold segmentation. In addition, there is a misclassification of water body and glacier. As a simple and efficient semantic segmentation network, U-Net has been widely used in many fields of image processing. This paper performs an improved semantic segmentation network Deep U-Net for glacier identification using Landsat 8 OLI image as the data source, and compares it with the traditional NDSI glacier identification method. The identification results are validated by the glacier label data produced by visual interpretation. The results indicate that the proposed method achieves an identification accuracy of 97.27%, which is higher than the NDSI glacier identification method. It can effectively exclude the interference of water bodies on glacier identification, and has a higher degree of automation.
LAKE ICE DETECTION FROM SENTINEL-1 SAR WITH DEEP LEARNING
Authors: Tom M. , Aguilar R. , Imhof P. , Leinss S. , Baltsavias E. , Schindler K.
Published: August 2020

Abstract. Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.
ESTIMATION OF CANOPY HEIGHT AND BIOMASS OF MISCANTHUS SINENSIS IN SEMI-NATURAL GRASSLAND USING TIME-SERIES UAV DATA
Authors: Miura N. , Yamada S. , Niwa Y.
Published: August 2020

Abstract. Grasslands are important ecosystems to provide various economic and ecological services. In Japan, grassland of Miscanthus sinensis, which is a tall, perennial grass species, has been one of the symbolic landscape and require efficient monitoring system for better management. In this study, canopy height and biomass of M. sinensis in semi-natural grassland are estimated using time-series UAV imagery and structure from motion (SfM) and multi-view stereo (MVS) technique. The effect of complex topography on estimation of the canopy height and biomass is analysed as well as monitoring growth of M. sinensis. The results showed that UAV derived maximum canopy height and biomass have significant correlation with vegetation survey data producing R2 value of 0.92 and 0.78, respectively. The effect of topographic landforms was found to be smallest on top of the hill, followed by slope. Valley-like sunken place was affected worst. Analysis using time-series UAV data revealed that growth of M. sinensis is different between the landforms, and the best time to estimate its biomass was different between them. In order to accurately estimate canopy height and biomass of tall grass species such as M. sinensis, it is important to take plant growth stage into consideration as well as topographic landforms.
CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES
Authors: Picoli M. C. A. , Simoes R. , Chaves M. , Santos L. A. , Sanchez A. , Soares A. , Sanches I. D. , Ferreira K. R. , Queiroz G. R.
Published: August 2020

Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.
PVANET-HOUGH: DETECTION AND LOCATION OF CENTER PIVOT IRRIGATION SYSTEMS FROM SENTINEL-2 IMAGES
Authors: Tang J. W. , Arvor D. , Corpetti T. , Tang P.
Published: August 2020

Abstract. Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, center pivot irrigation systems are popular in many countries. Monitoring the distribution of center pivot irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate center pivot irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of center pivot irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.
A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY
Authors: Tonion F. , Pirotti F. , Faina G. , Paltrinieri D.
Published: August 2020

Abstract. Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one transect as training and another as validation. Each set was composed by a total of ~6000 points. To estimate water depth two methods were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical applications.
GLACIAL LAKE EVOLUTION BASED ON REMOTE SENSING TIME SERIES: A CASE STUDY OF TSHO ROLPA IN NEPAL
Authors: Peppa M. V. , Maharjan S. B. , Joshi S. P. , Xiao W. , Mills J. P.
Published: August 2020

Abstract. Himalayan glaciers have retreated rapidly in recent years. Resultant glacial lakes in the region pose potential catastrophic threats to downstream communities, especially under a changing climate. The potential for Glacial Lake Outburst Floods (GLOFs) has increased and studies have assessed the risks of those in Nepal and prioritised several glacial lakes for urgent and closer investigation. The risk posed by the Tsho Rolpa Glacial Lake is one of the most serious in Nepal. To investigate the feasibility of high-frequency monitoring of glacial lake evolution by remote sensing, this paper proposes a workflow for automated glacial lake boundary extraction and evolution using a time series of Sentinel optical imagery. The waterbody is segmented and vectorised using bimodal histograms from water indices. The vectorised lake boundary is validated against reference data extracted from rigorous contemporary unmanned aerial vehicle (UAV)-based photogrammetric survey. Lake boundaries were subsequently extracted at four different epochs to evaluate the evolution of the lake, especially at the glacier terminus. The final lake area was estimated at 1.61 km2, significantly larger than the areal extent last formally reported. A 0.99 m/day maximum, and a 0.45 m/day average, horizontal glacier retreat rates were estimated. The reported research has demonstrated the potential of remote sensing time series to monitor glacial lake evolution, which is particularly important for lakes in remote mountain regions that are otherwise difficult to access.
METHOD OF IMPROVED TDICCD DESIGN BASED ON SENSOR CLOCKING
Authors: Wan M. , Guo Y. , Li S. , Liu T.
Published: August 2020

Abstract. The final output signal quality of TDICCD is related to the key parameters such as working mode, output mode, signal-to-noise ratio, dynamic range and so on. We can improve these parameters of TDICCD by reasonably designing the sensor clocking. Therefore, this text discussed four methods of improved TDICCD sensor clocking to solve some problem in TDICCD application based on using the principle of TDICCD. The technique of TDICCD Taps merging can reduce the number of TDICCD Taps, which helps to reduce the size of the TDICCD rear-end circuit significantly; The technique of TDICCD continuous transfer clocking can improve the charge transfer efficiency, which helps to promote the final signal-to-noise ratio; The technique of pixel binning clocking can enlarge the dynamic range of image; The technique of TDICCD area-array working mode can extend the field of TDICCD working; The principle, derivation process, clocking sequence diagram and application range of these clocking design schemes are given in this paper. At the same time, it also explains its actual effect and the matters to be noted.
MOVING OBJECT DETECTION METHOD OF VIDEO SATELLITE BASED ON TRACKING CORRECTION DETECTION
Authors: Yang X. , Li F. , Lu M. , Xin L. , Lu X. , Zhang N.
Published: August 2020

Abstract. It is the focus of current research that how to realize high precision and real-time dynamic monitoring and tracking of moving targets by video satellites because of instantaneous and dynamic continuous observation of targets in a certain area by the video satellites. The existing detection and tracking methods for moving objects have target misdetection and missed detection, which reduces the accuracy of moving object detection. In this paper, a Tracking Correction Detection Correction (TCD) method is proposed to solve these problems. Firstly, the background model is established by using the improved ViBe target detection algorithm, and the moving target mask is obtained by adaptive threshold calculation. By using pyramid structure iterative algorithm, the moving object can be classified as noise or real object according to the set of detection results of different detection windows. The high-order correlation vector tracking method is used to modify the detection result of the moving target acquired in the previous frame, and finally the accurate detection result of the moving target is obtained. The comparison analysis between the frame difference (FD) method, GMM method, ViBe method and TCD method shows that the TCD method has better robustness for noise, light and background dynamic changes, and the test results of TCD method are more complete and the real-time is better. It is proved by this work that the accuracy of the target detection of TCD method has reached 85%, which has a high engineering application value.
QUALITY ASSESSMENT BASED ON ACCURATE POSITIONING PRODUCTION RESULTS OF NATIONAL GEOMORPHIC DATA
Authors: Yin S. , Zhang L. , Zhao H. T. , Chen H. P. , Gao W. C.
Published: August 2020

Abstract. Using high-precision DEM and high-resolution image, the geomorphic type boundary is accurately located on the basis of 1:1 million geomorphic type data boundary in China. For the first time, a national geomorphic data set with positioning accuracy of 1:250000 scale mapping accuracy has been formed. Based on the production technology and process of 1:250000 national geomorphic data set, as well as the content index and requirement of geomorphic statistical analysis, this paper designs and realizes the quality inspection model, evaluation index and evaluation method of 1:250000 national geomorphic data set. The results of 60 1:250000 geomorphic data as samples show that the quality of geomorphic data can be truly reflected.
CLIMATE CHANGES AND LANDSCAPE RESPONSES OF CHINA DURING THE PAST 40 YEARS (1979–2018) UNDER KÖPPEN-GEIGER CLIMATE CLASSIFICATION
Authors: Feng Y. , Du S.
Published: August 2020

Abstract. Under the background of global warming, China with diverse climate types is experiencing dramatic climate change. In this study, we produced a series of climate type map of China at 0.1° resolution from 1979 to 2018 using Köppen-Geiger climate classification, which provided continuous fine-scale decadal climate classification data for climate researches in China. Based on these climate maps, we divided China into four main climate zones: arid climate zone located on the north of Tibetan Plateau and west of Inner Mongolian Plateau, temperate climate zones in southern China, cold climate zone occupied most of Northeast Plain and North China Plain, and polar climate zone on Tibetan Plateau. The distribution of main climate classes in China have not changed significantly over 40 years, while the climate change mainly occurs at the levels of climate type and subtype changes. The frequency of climate changes shows the climate sensitivity of the region, and we identified the transition areas of climate zones with a high sensitivity to climate change. The change of climate types shows an obvious trend of rising temperature in all climate zones of China and increasing precipitation in most climate zones of China (the cold climate zone shows no significant dry or wet change). Overall, the climate in China is generally getting warmer and wetter in the past 40 years. Furthermore, we analysed the landscape responses on climate change with land cover data, e.g. the vegetation type variations in southern China and the snow cover fluctuations on Tibetan Plateau.