bluuebunny/crossref_metadata_2025 huggingface dataset:
APPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD
Authors: Cai S. , Zhang W. , Qi J. , Wan P. , Shao J. , Shen A.
Published: April 2018

Abstract. Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.
GRASSLAND NPP MONITORING BASED ON MULTI-SOURCE REMOTE SENSING DATA FUSION
Authors: Cai Y. R. , Zheng J. H. , Du M. J. , Mu C. , Peng J.
Published: April 2018

Abstract. Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006–2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.
SPATIO-TEMPORAL VARIATIONS OF SOIL WATER USE IN THE GROWING SEASON IN NORTHEAST CHINA USING MODIS DATA
Authors: Chang s. , Huang F. , Li B. , Qi H. , Zhai H.
Published: April 2018

Abstract. Water use efficiency is known as an important indicator of carbon and water cycle and reflects the transformation capacity of vegetation water and nutrients into biomass. In this study, we presented a new indicator of water use efficiency, soil water use level (SWUL), derived from satellite remote sensing based gross primary production and the Visible and Shortwave Infrared Drought Index (VSDI). SWUL based on MODIS data was calculated for the growing season of 2014 in Northeast China, and the spatial pattern and the variation trend were analyzed. Results showed that the highest SWUL was observed in forestland with the value of 36.65. In cropland and grassland, the average SWUL were 26.18 and 29.29, respectively. SWUL showed an increased trend in the first half period of the growing season and peaked around the 200th day. After the 220th day, SWUL presented a decreasing trend. Compared to the soil water use efficiency (SWUE), SWUL might depict the water use status at finer spatial resolution. The new indicator SWUL can help promote understanding the water use efficiency for regions of higher spatial heterogeneity.
APPLICATION OF MACHINE LEARNING IN URBAN GREENERY LAND COVER EXTRACTION
Authors: Qiao X. , Li L. L. , Li D. , Gan Y. L. , Hou A. Y.
Published: April 2018

Abstract. Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.
A VOXEL-BASED FILTERING ALGORITHM FOR MOBILE LIDAR DATA
Authors: Qin H. , Guan G. , Yu Y. , Zhong L.
Published: April 2018

Abstract. This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.
EXTRACTION AND ANALYSIS OF MAJOR AUTUMN CROPS IN JINGXIAN COUNTY BASED ON MULTI - TEMPORAL GF - 1 REMOTE SENSING IMAGE AND OBJECT-ORIENTED
Authors: Ren B. , Wen Q. , Zhou H. , Guan F. , Li L. , Yu H. , Wang Z.
Published: April 2018

Abstract. The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.
ACCURACY ASSESSMENT OF THE GLOBELAND30 DATASET IN JIANGXI PROVINCE
Authors: Ren H. , Cai G. , Zhao G. , Li Z.
Published: April 2018

Abstract. The Globeland30 dataset is the most highly spatial resolution global land cover mapping product, which developed by the National Geomatics Center of China (NGCC) in 2015. It plays a significant role in environmental monitoring, climate change, and ecosystem assessment, etc. In this study, Jiangxi province was selected as our study area, the 1 : 100000 land use data in 2010 was employed as the reference data. We aim to examine the accuracy of the Globeland30 from three methods, including area error analysis, shape consistency analysis and confusion matrix. The results show as follows: The land cover types in the study area are primarily occupied by the cultivated land and forest, and secondarily by grassland, water bodies and artificial surfaces. The area error of cultivated land, forest and water bodies are all less than 13 %; The general conformance of the shape consistency reaches to 67 %, but the shape consistency of every land type differs to a large degree, the best shape consistency of forests is up to 75 %; The confusion matrix is obtained in two cases of different class boundary with buffer and no buffer area. It is found that the overall accuracy and kappa coefficient of GlobeLand30 are improved with buffer area. The value of overall accuracy is higher than 78 %, the value of kappa coefficient is higher than 0.52.
URBAN SPATIAL ECOLOGICAL PERFORMANCE BASED ON THE DATA OF REMOTE SENSING OF GUYUAN
Authors: Ren X.-J. , Chen X.-J. , Ma Q.
Published: April 2018

Abstract. The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.
THE STUDY OF RESIDENTIAL AREAS EXTRACTION BASED ON GF-3 TEXTURE IMAGE SEGMENTATION
Authors: Shao G. , Luo H. , Tao X. , Ling Z. , Huang Y.
Published: April 2018

Abstract. The study chooses the standard stripe and dual polarization SAR images of GF-3 as the basic data. Residential areas extraction processes and methods based upon GF-3 images texture segmentation are compared and analyzed. GF-3 images processes include radiometric calibration, complex data conversion, multi-look processing, images filtering, and then conducting suitability analysis for different images filtering methods, the filtering result show that the filtering method of Kuan is efficient for extracting residential areas, then, we calculated and analyzed the texture feature vectors using the GLCM (the Gary Level Co-occurrence Matrix), texture feature vectors include the moving window size, step size and angle, the result show that：window size is 11*11, step is 1, and angle is 0°, which is effective and optimal for the residential areas extracting. And with the FNEA (Fractal Net Evolution Approach), we segmented the GLCM texture images, and extracted the residential areas by threshold setting. The result of residential areas extraction verified and assessed by confusion matrix. Overall accuracy is 0.897, kappa is 0.881, and then we extracted the residential areas by SVM classification based on GF-3 images, the overall accuracy is less 0.09 than the accuracy of extraction method based on GF-3 Texture Image Segmentation. We reached the conclusion that，residential areas extraction based on GF-3 SAR texture image multi-scale segmentation is simple and highly accurate. although, it is difficult to obtain multi-spectrum remote sensing image in southern China, in cloudy and rainy weather throughout the year, this paper has certain reference significance.
ESTIMATION OF STATIC COULOMB STRESS CHANGE AND STRONG MOTION SIMULATION FOR JIUZHAIGOU 7.0 EARTHQUAKE BASE ON SENTINEL-1 INSAR DATA INVERSION
Authors: Shen W. H. , Luo Y. , Jiao Q. S.
Published: April 2018

Abstract. On August 8, 2017, an earthquake of M 7.0 occurred at Jiuzhaigou. Based on the Sentinel-1 satellite InSAR data, we obtained coseismic deformation field and inverted the source slip model. Results show that this event is dominated by strike slip, and the total released seismic moment is 8.06 × 1018 Nm, equivalent to an earthquake of Mw ~ 6.57. We calculated static stress changes along strike and dip direction, and the static stress analysis show that the average stress drop are at low level, which may be responsible for the low level of ground motion during Jiuzhaigou earthquake. The coseismic Coulomb stress changes are calculated base on the inverted slip model, which revealed that 82.59 % of aftershocks are located in the Coulomb stress increasing area, 78.42 % of total aftershocks may be triggered by the mainshock aftershock, indicating that the mainshock has a significant triggering effect on the subsequent aftershocks. Based on stochastic finite fault model, we simulated regional peak ground acceleration (PGA), peak ground velocity (PGV) and the intensity, and results could capture basic features associated with the ground motion patterns. Moreover, the simulated results reflect the obvious rupture directivity effect.
THE EXTRACTION OF POST-EARTHQUAKE BUILDING DAMAGE INFORMATIOM BASED ON CONVOLUTIONAL NEURAL NETWORK
Authors: Chen M. , Wang X. , Dou A. , Wu X.
Published: April 2018

Abstract. The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixel-based and object-oriented methods have some shortcoming in extracting information of object. Pixel-based method can’t make fully use of contextual information of objects. Object-oriented method faces problem that segmentation of image is not ideal, and the choice of feature space is difficult. In this paper, a new stratage is proposed which combines Convolution Neural Network (CNN) with imagery segmentation to extract building damage information from remote sensing imagery. the key idea of this method includes two steps. First to use CNN to predicate the probability of each pixel and then integrate the probability within each segmentation spot. The method is tested through extracting the collapsed building and uncollapsed building from the aerial image which is acquired in Longtoushan Town after Ms 6.5 Ludian County, Yunnan Province earthquake. The results show that the proposed method indicates its effectiveness in extracting damage information of buildings after earthquake.
RESPONSE OF ALPINE GRASSLAND VEGETATION PHENOLOGY TO SNOW ACCUMULATION AND MELT IN NAMCO BASIN
Authors: Chen S. , Cui X. , Liang T.
Published: April 2018

Abstract. Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.
WAVELET FUSION FOR CONCEALED OBJECT DETECTION USING PASSIVE MILLIMETER WAVE SEQUENCE IMAGES
Authors: Chen Y. , Pang L. , Liu H. , Xu X.
Published: April 2018

Abstract. PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods，firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean filter, threshold segmentation and edge detection. The experimental results show that this method improves the detection effect of concealed objects by selecting the most relevant images from PMMW sequence images and using wavelet fusion to enhance the information of the concealed objects. The method can be effectively applied to human body concealed object detection in millimeter wave video.
ROAD DAMAGE EXTRACTION FROM POST-EARTHQUAKE UAV IMAGES ASSISTED BY VECTOR DATA
Authors: Chen Z. , Dou A.
Published: April 2018

Abstract. Extraction of road damage information after earthquake has been regarded as urgent mission. To collect information about stricken areas, Unmanned Aerial Vehicle can be used to obtain images rapidly. This paper put forward a novel method to detect road damage and bring forward a coefficient to assess road accessibility. With the assistance of vector road data, image data of the Jiuzhaigou Ms7.0 Earthquake is tested. In the first, the image is clipped according to vector buffer. Then a large-scale segmentation is applied to remove irrelevant objects. Thirdly, statistics of road features are analysed, and damage information is extracted. Combining with the on-filed investigation, the extraction result is effective.
A MODEL STUDY OF SMALL-SCALE WORLD MAP GENERALIZATION
Authors: Cheng Y. , Yin Y. , Li C. M. , Wu W. , Guo P. P. , Ma X. L. , Hu F. M.
Published: April 2018

Abstract. With the globalization and rapid development every filed is taking an increasing interest in physical geography and human economics. There is a surging demand for small scale world map in large formats all over the world. Further study of automated mapping technology, especially the realization of small scale production on a large scale global map, is the key of the cartographic field need to solve. In light of this, this paper adopts the improved model (with the map and data separated) in the field of the mapmaking generalization, which can separate geographic data from mapping data from maps, mainly including cross-platform symbols and automatic map-making knowledge engine. With respect to the cross-platform symbol library, the symbol and the physical symbol in the geographic information are configured at all scale levels. With respect to automatic map-making knowledge engine consists 97 types, 1086 subtypes, 21845 basic algorithm and over 2500 relevant functional modules.In order to evaluate the accuracy and visual effect of our model towards topographic maps and thematic maps, we take the world map generalization in small scale as an example. After mapping generalization process, combining and simplifying the scattered islands make the map more explicit at 1 : 2.1 billion scale, and the map features more complete and accurate. Not only it enhance the map generalization of various scales significantly, but achieve the integration among map-makings of various scales, suggesting that this model provide a reference in cartographic generalization for various scales.
SEMI AUTOMATED LAND COVER LAYER UPDATING PROCESS UTILIZING SPECTRAL ANALYSIS AND GIS DATA FUSION
Authors: Cohen L. , Keinan E. , Yaniv M. , Tal Y. , Felus A. , Regev R.
Published: April 2018

Abstract. Technological improvements made in recent years of mass data gathering and analyzing, influenced the traditional methods of updating and forming of the national topographic database. It has brought a significant increase in the number of use cases and detailed geo information demands. Processes which its purpose is to alternate traditional data collection methods developed in many National Mapping and Cadaster Agencies. There has been significant progress in semi-automated methodologies aiming to facilitate updating of a topographic national geodatabase. Implementation of those is expected to allow a considerable reduction of updating costs and operation times. Our previous activity has focused on building automatic extraction (Keinan, Zilberstein et al, 2015). Before semiautomatic updating method, it was common that interpreter identification has to be as detailed as possible to hold most reliable database eventually. When using semi-automatic updating methodologies, the ability to insert human insights based knowledge is limited. Therefore, our motivations were to reduce the created gap by allowing end-users to add their data inputs to the basic geometric database. In this article, we will present a simple Land cover database updating method which combines insights extracted from the analyzed image, and a given spatial data of vector layers. The main stages of the advanced practice are multispectral image segmentation and supervised classification together with given vector data geometric fusion while maintaining the principle of low shape editorial work to be done. All coding was done utilizing open source software components.
URBAN MONITORING BASED ON SENTINEL-1 DATA USING PERMANENT SCATTERER INTERFEROMETRY AND SAR TOMOGRAPHY
Authors: Crosetto M. , Budillon A. , Johnsy A. , Schirinzi G. , Devanthéry N. , Monserrat O. , Cuevas-González M.
Published: April 2018

Abstract. A lot of research and development has been devoted to the exploitation of satellite SAR images for deformation measurement and monitoring purposes since Differential Interferometric Synthetic Apertura Radar (InSAR) was first described in 1989. In this work, we consider two main classes of advanced DInSAR techniques: Persistent Scatterer Interferometry and Tomographic SAR. Both techniques make use of multiple SAR images acquired over the same site and advanced procedures to separate the deformation component from the other phase components, such as the residual topographic component, the atmospheric component, the thermal expansion component and the phase noise. TomoSAR offers the advantage of detecting either single scatterers presenting stable proprieties over time (Persistent Scatterers) and multiple scatterers interfering within the same range-azimuth resolution cell, a significant improvement for urban areas monitoring. This paper addresses a preliminary inter-comparison of the results of both techniques, for a test site located in the metropolitan area of Barcelona (Spain), where interferometric Sentinel-1 data were analysed.
HIGH RESOLUTION SAR IMAGING EMPLOYING GEOMETRIC FEATURES FOR EXTRACTING SEISMIC DAMAGE OF BUILDINGS
Authors: Cui L. P. , Wang X. P. , Dou A. X. , Ding X.
Published: April 2018

Abstract. Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.
QUALITY EVALUATION OF LAND-COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
Authors: Dang Y. , Zhang J. , Zhao Y. , Luo F. , Ma W. , Yu F.
Published: April 2018

Abstract. Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can’t be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image’s membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.
STUDY ON BUILDING EXTRACTION FROM HIGH-RESOLUTION IMAGES USING MBI
Authors: Ding Z. , Wang X. Q. , Li Y. L. , Zhang S. S.
Published: April 2018

Abstract. Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.
ROBUST FEATURE MATCHING IN TERRESTRIAL IMAGE SEQUENCES
Authors: Abbas A. , Ghuffar S.
Published: April 2018

Abstract. From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.
EDGE-BASED LOCALLY AGGREGATED DESCRIPTORS FOR IMAGE CLUSTERING
Authors: Dong Y. , Fan D. , Ma Q. , Ji S. , Lei R.
Published: April 2018

Abstract. The current global image descriptors are mostly obtained by using the local image features aggregation, which fail to take full account of the details of the image, resulting in the loss of the semantic content information. It cannot be well used to make a good distinction between the high similarity images. In this paper, a new method of image representation, which can express the whole semantics and detail features of the image, is proposed by combining the edge features of the image. It is used to make a global description of the images and then clustering. The experimental results show that the proposed method is capable of clustering of the similarity images with high accuracy and low error rate.
ENDMEMBER EXTRACTION OF HIGHLY MIXED DATA USING L1 SPARSITY-CONSTRAINED MULTILAYER NONNEGATIVE MATRIX FACTORIZATION
Authors: Fang H. , Li A. H. , Xu H. X. , Wang T. , Jiang K. , Su Y. Z. , Chang H. W.
Published: April 2018

Abstract. Due to the limited spatial resolution of remote hyperspectral sensors, pixels are usually highly mixed in the hyperspectral images. Endmember extraction refers to the process identifying the pure endmember signatures from the mixture, which is an important step towards the utilization of hyperspectral data. Nonnegative matrix factorization (NMF) is a widely used method of endmember extraction due to its effectiveness and convenience. While most NMF-based methods have single-layer structures, which may have difficulties in effectively learning the structures of highly mixed and complex data. On the other hand, multilayer algorithms have shown great advantages in learning data features and been widely studied in many fields. In this paper, we presented a L1 sparsityconstrained multilayer NMF method for endmember extraction of highly mixed data. Firstly, the multilayer NMF structure was obtained by unfolding NMF into a certain number of layers. In each layer, the abundance matrix was decomposed into the endmember matrix and abundance matrix of the next layer. Besides, to improve the performance of NMF, we incorporated sparsity constraints to the multilayer NMF model by adding a L1 regularizer of the abundance matrix to each layer. At last, a layer-wise optimization method based on NeNMF was proposed to train the multilayer NMF structure. Experiments were conducted on both synthetic data and real data. The results demonstrate that our proposed algorithm can achieve better results than several state-of-art approaches.
STUDY ON REMOTE SENSING IMAGE CHARACTERISTICS OF ECOLOGICAL LAND: CASE STUDY OF ORIGINAL ECOLOGICAL LAND IN THE YELLOW RIVER DELTA
Authors: An G. Q.
Published: April 2018

Abstract. Takes the Yellow River Delta as an example, this paper studies the characteristics of remote sensing imagery with dominant ecological functional land use types, compares the advantages and disadvantages of different image in interpreting ecological land use, and uses research results to analyse the changing trend of ecological land in the study area in the past 30 years. The main methods include multi-period, different sensor images and different seasonal spectral curves, vegetation index, GIS and data analysis methods. The results show that the main ecological land in the Yellow River Delta included coastal beaches, saline-alkaline lands, and water bodies. These lands have relatively distinct spectral and texture features. The spectral features along the beach show characteristics of absorption in the green band and reflection in the red band. This feature is less affected by the acquisition year, season, and sensor type. Saline-alkali land due to the influence of some saline-alkaline-tolerant plants such as alkali tent, Tamarix and other vegetation, the spectral characteristics have a certain seasonal changes, winter and spring NDVI index is less than the summer and autumn vegetation index. The spectral characteristics of a water body generally decrease rapidly with increasing wavelength, and the reflectance in the red band increases with increasing sediment concentration. In conclusion, according to the spectral characteristics and image texture features of the ecological land in the Yellow River Delta, the accuracy of image interpretation of such ecological land can be improved.
JOINT CALIBRATION OF 3D LASER SCANNER AND DIGITAL CAMERA BASED ON DLT ALGORITHM
Authors: Gao X. , Li M. , Xing L. , Liu Y.
Published: April 2018

Abstract. Design a calibration target that can be scanned by 3D laser scanner while shot by digital camera, achieving point cloud and photos of a same target. A method to joint calibrate 3D laser scanner and digital camera based on Direct Linear Transformation algorithm was proposed. This method adds a distortion model of digital camera to traditional DLT algorithm, after repeating iteration, it can solve the inner and external position element of the camera as well as the joint calibration of 3D laser scanner and digital camera. It comes to prove that this method is reliable.
EARTHQUAKE BUILDING DAMAGE MAPPING BASED ON FEATURE ANALYZING METHOD FROM SYNTHETIC APERTURE RADAR DATA
Authors: An L. , Zhang J. , Gong L.
Published: April 2018

Abstract. Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.
A NEW REALIZATION OF THE GLOBAL LUNAR REFERENCE FRAME BASED ON CO-REGISTERED LOLA TRACKS
Authors: Gläser P. , Haase I. , Oberst J.
Published: April 2018

Abstract. We present a method that aligns lunar south and north pole LOLA DTMs using selected LOLA tracks and co-registration techniques. The selected LOLA tracks were then co-registered to the aligned polar DTMs with the aim to create a new LOLA frame of high relative accuracy. At the poles the relative accuracy of the resulting LOLA frame improved in comparison with the original LOLA frame, especially at the north pole. At lower latitudes on the lunar near side we could show that we achieve smaller residuals between our LOLA frame and a photogrammetrically derived reference DTM than with the original LOLA frame. On the far side we could not achieve better results which we believe is stemming from the generally less accurate orbit knowledge there. From the aligned polar DTMs we were able to derive a polar radius of 1738,049 km.
TARGETS MASK U-NET FOR WIND TURBINES DETECTION IN REMOTE SENSING IMAGES
Authors: Han M. , Wang H. , Wang G. , Liu Y.
Published: April 2018

Abstract. To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. This convolution neural network (CNN), which is carefully designed to be a wide-field detector, models the pixel class assignment to wind turbines and their context information. The shadow, which is the context information of the target in this study, has been regarded as part of a wind turbine instance. We have trained the target mask U-Net on training dataset, which is composed of down sampled image blocks and instance mask blocks. Some post-processes have been integrated to eliminate wrong spots and produce bounding boxes of wind turbine instances. The evaluation metrics prove the reliability and effectiveness of our method for the average F1-score of our detection method is up to 0.97. The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method.
RESEARCH ON HIGH ACCURACY DETECTION OF RED TIDE HYPERSPECRRAL BASED ON DEEP LEARNING CNN
Authors: Hu Y. , Ma Y. , An J.
Published: April 2018

Abstract. Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.
THE FEASIBILITY EVALUATION OF LAND USE CHANGE DETECTION USING GAOFEN-3 DATA
Authors: Huang G. , Sun Y. , Zhao Z.
Published: April 2018

Abstract. GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.
ACCURACY DIMENSIONS IN REMOTE SENSING
Authors: Barsi Á. , Kugler Zs. , László I. , Szabó Gy. , Abdulmutalib H. M.
Published: April 2018

Abstract. The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.
SCALING PATTERNS OF NATURAL URBAN PLACES AS A RULE FOR ENHANCING THEIR URBAN FUNCTIONALITY USING TRAJECTORY DATA
Authors: Jia T. , Yu X.
Published: April 2018

Abstract. With the availability of massive trajectory data, it is highly valuable to reveal their activity information for many domains such as understanding the functionality of urban regions. This article utilizes the scaling patterns of human activities to enhance functional distribution of natural urban places. Specifically, we proposed a temporal city clustering algorithm to aggregate the stopping locations into natural urban places, which are reported to follow remarkable power law distributions of sizes and obey a universal law of economy of scale on human interactions with urban infrastructure. Besides, we proposed a novel Bayesian inference model with damping factor to estimate the most likely POI type associated with a stopping location. Our results suggest that hot natural urban places could be effectively identified from their scaling patterns and their functionality can be very well enhanced. For instance, natural urban places containing airport or railway station can be highly stressed by accumulating the massive types of human activities.
DETECTING WATER BODIES IN LANDSAT8 OLI IMAGE USING DEEP LEARNING
Authors: Jiang W. , He G. , Long T. , Ni Y.
Published: April 2018

Abstract. Water body identifying is critical to climate change, water resources, ecosystem service and hydrological cycle. Multi-layer perceptron(MLP) is the popular and classic method under deep learning framework to detect target and classify image. Therefore, this study adopts this method to identify the water body of Landsat8. To compare the performance of classification, the maximum likelihood and water index are employed for each study area. The classification results are evaluated from accuracy indices and local comparison. Evaluation result shows that multi-layer perceptron(MLP) can achieve better performance than the other two methods. Moreover, the thin water also can be clearly identified by the multi-layer perceptron. The proposed method has the application potential in mapping global scale surface water with multi-source medium-high resolution satellite data.
IMPROVING THE INTEROPERABILITY OF DISASTER MODELS: A CASE STUDY OF PROPOSING FIREML FOR FOREST FIRE MODEL
Authors: Jiang W. , Wang F. , Meng Q. , Li Z. , Liu B. , Zheng X.
Published: April 2018

Abstract. This paper presents a new standardized data format named Fire Markup Language (FireML), extended by the Geography Markup Language (GML) of OGC, to elaborate upon the fire hazard model. The proposed FireML is able to standardize the input and output documents of a fire model for effectively communicating with different disaster management systems to ensure a good interoperability. To demonstrate the usage of FireML and testify its feasibility, an adopted forest fire spread model being compatible with FireML is described. And a 3DGIS disaster management system is developed to simulate the dynamic procedure of forest fire spread with the defined FireML documents. The proposed approach will enlighten ones who work on other disaster models' standardization work.