20 June 2018, Volume 33 Issue 3
    

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  • Application Progress on Goldstone Solar System Radar and Its Inspiration for Deep Space Exploration in China
    Li Dewei，Jiang Liming，Jiang Houjun
    Remote Sensing Technology and Application. 2018, 33(3): 377-386. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0377
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    Goldstone Solar System Radar(GSSR) has been played an important role in international deep space exploration and widely used in lunar terrain mapping，Mars exploration，asteroids orbit determination，deep space aircraft measurement or control，etc.We reviewed the background and summarized the present development situation of the GSSR system which is the only fully steerable imaging radar system in the world for planetary and small\|body targets.Then，we analyzed the composition of the GSSR system and its imaging principle of the near\|Earth objects in details.In addition，we emphatically introduced the new applications of GSSR in deep space exploration.GSSR has provided an inspiration for the deep space exploration in China and given a valuable reference to Lunar\|based Synthetic Aperture Radar(SAR) for Earth observation.
  • Effects of the Relocation of Chongqing Iron and Steel Group Industrial Area on Regional Ecological Quality
    Wang Lin，Xu Hanqiu，Li Sheng
    Remote Sensing Technology and Application. 2018, 33(3): 387-397. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0387
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    Urbanization is typically accompanied by the reconstruction or relocation of heavy industrial areas due to limited space and high standard environmental requirements.Taking "environment\|friendly relocation" of Chongqing Iron and Steel Group as an example，this study quantitatively compares and analyzes changes of the thermal and ecological conditions before and after the relocation.Main biophysical properties of the study area and land surface temperature(LST) were retrieved from the multi\|temporal Landsat serial satellite images.Urban Heat Island Index(URI) and Remote Sensing based Ecological Index(RSEI) were employed for the study.The results reveal that urban heat island effect of the industrial area was significantly mitigated and the ecological quality was significantly improved after the relocation，suggested by the decline of URI value from 0.387 in 2005 to 0.128 in 2014(a drop of 66.7%)，and the rise of RSEI value from 0.398 to 0.553 during the same period(an increased of 38.9%).This is due largely to the halting of steel production，change of land cover types and properties caused by the “environment\|friendly relocation” of the industrial area.In general，the halting of steel production，the decrease of impervious surface and the increase of vegetation coverage can mitigate the heat island effect and improve the ecological quality.The result of this study can provide a useful case for reconstruction or relocation of urban heavy industrial area and promotion of city’s healthy sustainable development.



  • Effects of Landscape Pattern on Annual Variation  of Thermal Environment in Hangzhou
    Lu Huimin，Li Fei，Zhang Meiliang，Yang Gang，Sun Weiwei
    Remote Sensing Technology and Application. 2018, 33(3): 398-407. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0398
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    Taking the Hangzhou city as an example，this paper retrieves the urban land surface temperature (LST) using 4 Landsat ETM+/OLI_TIRS images and investigates the effects of landscape pattern on the urban thermal environment change.The hot spot analysis was used to identify both the urban heat island and cold island.Landscape pattern indices were adopted to analyze the relationship between the change of the thermal environment and the landscape pattern.Analysis results show that：(1) The proportions of area in urban heat island and cold island in Hangzhou increase first and then decrease with the alternating four seasons；The urban heat island of Hangzhou is the most significant in summer，and the urban cold island effect is more dominant in autumn；(2) Throughout the year，all kinds of landscape has the highest average land surface temperature in summer and the lowest in winter；As for a variety of landscapes，the construction land has the highest average land surface temperature，while，the water body and forest have the relatively low average land surface temperature；(3)On the landscape level，the selected landscape pattern indices are significantly correlated with average land surface temperature in four seasons，the strength of correlation fluctuates with alternating four seasons and the enlargement of analysis window；On the class level，landscape pattern indices of construction land，water body and forest are significantly and highly correlated with average land surface temperature in different seasons.The research in our paper could help to lay out construction land rationally and execute planning and design on urban green space and waters to effectively alleviate the urban heat island effect of Hangzhou.
  • A Novel Method to Make Feature Space Adequately Complex：A Case Study on the Classification of Underlying Surfacein Urban Area from Remote Sensing Data
    Ren Zhehao，Zhou Jianhua
    Remote Sensing Technology and Application. 2018, 33(3): 408-417. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0408
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    An adequately complex feature space is indispensible as classifying underlying surface in urban area from remote sensing data.Therefore，more independent descriptors are required to increase dimension of the feature space.However，pervasive basic descriptors，as we all know，are usually not enough to construct the feature space.Three novel and pervasive approaches to getting new descriptors by extending these basic descriptors are explored in this paper.They are introduced as follows.1) Take standard deviation of neighbourhood elements in a basic descriptor as weight to indicate neighbourhood\|based multi\|scale information for the center pixel and name the approach as NMIS.The NMIS\|extended value of the center pixel is summed from several layers.These layers are different from each other only in the size of neighbourhood in which the standard deviation is calculated.2) Form multi\|scale texture layers by using a set of size\|given structure elements and name this approach as STIM.Each layer is a STIM\|extended descriptor and serves as an independent descriptor in the feature space.With a set of STIM\|extended descriptors having a basic texture descriptor as their common source，the difference in coarseness between classes can be identified.3) The third extending approach knows as polymorphic density dimension.The density dimension (De) is an algorithm for combining multiple basic features into a single descriptor to indicate geographical distribution of neighborhood elements carrying these features.Compared with previous De，a descriptor of the polymorphic De also combines multiple basic features but allows these features in different types (e.g.being spectrum and texture ones).The extending descriptor is independent from anyone of these combined features and able to be added into the feature space including these features.Accuracy assessment indicated that the average overall accuracy of classification with an extended\|descriptor\|involved input feature vector is 7.86% better than that with only basic\|descriptor\|involved one.
  • Effects of Different Spatial Resolution of Remote SensingImages on Estimation Accuracy of Urban Building Height
    Ding Zhe，Wang Xiaoqin，Wu Qunyong
    Remote Sensing Technology and Application. 2018, 33(3): 418-427. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0418
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    In this study，the remote sensing images of WorldView-2，GF-2，and GF-1，which cover Xiamen Software Park，were selected for study.A building and shadow extraction process suitable for different images was constructed，which applied object\|oriented approach and morphology ideas combined with spectral，shadow and shape constraints.Subsequently，the building heights of three different spatial resolutions of 0.5 m，1 m and 2 m were estimated by using the shadow length estimation method.Finally，the influence of image spatial resolution on building extraction accuracy and building height estimation accuracy was evaluated quantitatively.The main conclusions are as follows：(1) The improved building and shadow extraction process achieves higher extraction accuracy，but accuracy decreases slightly with the decrease of spatial resolution of images；(2) With the decrease of spatial resolution，the accuracy of building height estimation decreases gradually，but it does not show linear relationship.At the resolution increases from 1m to 0.5 m，the accuracy of building height estimation increases faster than the resolution increases from 2 m to 1 m；(3) GF-1 is more suitable for height estimation of high\|rise buildings and GF-2 is suitable for middle and high rise buildings，while WorldView\|2 has higher estimation accuracy for building height in different height ranges.
  • Radiometric Consistency between Landsat 8 OLIand Sentinel-2 MSI Imagery in Mountainous Terrain
    Zhong Hanxiao，Bian Jinhu，Li Ainong
    Remote Sensing Technology and Application. 2018, 33(3): 428-438. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0428
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    The combined use of multi\|sensor/multi\|temporal images provides more opportunities for long\|term land surface monitoring with high resolution and frequency requirements.However，as sensors differ in their orbital，spatial，or spectral configuration，uncertainty was introduced in the radiometric consistency of multi\|sourse images，and that becomes more outstanding in mountainous terrain with the sharp topographic relief.Therefore，a series of radiometric corrections need to be carry out before further application.The objective of this study was to indicate the radiometric consistency of Landsat\|8 OLI and Sentinel\|2 MSI images.Thus the radiometric differences between the corresponding bands of these two images acquired almost simultaneously by OLI and MSI over 2 areas at different latitude was calculated for the TOA reflectance images first.Then several radiometric corrections(atmospheric correction，BRDF correction and bandpass adjustment) were carried out successively and after each of them the radiometric differences were researched again to assess the performance of each correction method.The results first indicate that there is high radiometric consistency between OLI\|L1T and MSI\|L1C images with the R2greater than 0.9 for each band involved.Then higher consistency was found after the 6S atmospheric correction and C\|factor BRDF correction，while no remarkable improve was found after the fixed\|parameter bandpass adjustment.Furthermore，in area with great topographic relief，the radiometric consistency were higher for hillside facing the sun than hillside in shadow (the MAD of SWIR2 band was 0.010 and RMSD was 0.007 in sun\|light area，while the MAD was 0.005 and RMSD was 0.004 in shadowed area).The results point out that proper atmospheric correction，BRDF correction and bandpass adjustment could be used to improve the radiometric consistency，and topographic correction might also be carried out to balance the radiometric consistency differences between different hillsides.
  • Airborne Experiment of TG-2 Multi-angle Polarization Imager for Cloud Phase Identification
    Guo Junjie，Yao Zhigang
    Remote Sensing Technology and Application. 2018, 33(3): 439-448. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0439
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    Multi\|angle polarization measurement technology can be used to detect cloud and aerosol information，and it barely depends on prior temperature information.In order to verify capability of cloud phase classification from the TG\|2 Multi\|angle Polarization Imager developed independently by China，the results of first airborne observation experiment of Multi\|angle Polarization Imager for cloud observations were compared with the results of numerical simulation under typical conditions.The libRadtran model simulation shows that the water cloud has the maximum polarized radiation (primary rainbow) for scattering angles near 140°.The results based on radio sounding and microwave radiometer show that there are water cloud under 3.7 km height of the research area.Meanwhile，the Multi\|angle Polarization Imager accurately captures the character of maximum polarized radiation of water cloud droplet (primary rainbow) near 140° scattering angles.In addition，the corresponding MODIS observation data also shows there exists a large scale of water cloud near the research area.Above results comprehensively indicate the Multi\|angle Polarization Imager has the function of cloud phase recognition without relying on prior information，and this test lays the foundation for the further study of the space\|based polarization observations of cloud phase.
  • Study on Mechanism of Methane Column Change Influencing  Atmospheric Structure and Local Heavy Rain
    Zhu Jiashan，Wei Ming
    Remote Sensing Technology and Application. 2018, 33(3): 449-457. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0449
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    In order to study the relationship between atmospheric methane content and local strong convective weather，relationships among the methane content and the atmospheric structure and the local heavy precipitation are studied by using the L3 methane daily inversion product retrieved by AIRS.The results show that the distribution of total methane column content in atmosphere is strongly affected by the topography and terrain，and the methane content fluctuates in the complex topography area，which has a good inverse correlation with the precipitation.Methane has radiation and chemical activity which can affect the ground gas radiation，change the vertical distribution of atmospheric temperature，and increase the atmospheric instability.The change of methane column could be used as a precursor to the occurrence of heavy rainfall.It can provide the new view and a reference for exploring the mechanism of local heavy rain combined with multi\|source meteorological data，analysis of atmospheric power，heat，water vapor conditions.
  • Retrieval Rice Leaf Area Index Using Random Forest Algorithm based on GF-1 WFV Remote Sensing Data
    Liu Zhenbo，Zou Xian，Ge Yunjian，Chen Jian，Cao Yumeng
    Remote Sensing Technology and Application. 2018, 33(3): 458-464. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0458
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    Leaf Area Index(LAI) is an important indicator of vegetation growth and reflects the productivity of farmland ecosystems.In this study，rice LAI was mapping using LAI retrieved model based on rice vegetation indexes from multi\|temporal GF\|1 WFV and situ LAI measurements data obtained in different rice growing periods over rice fields taking Dongtai county，Jiangsu province as a case study.The LAI retrieval model was constructed using random forest algorithm(RF).Results showed that the RF model achieved high accuracy that the RMSE was 1.03 and the coefficients of determination(R2) between retrieved LAI and measured LAI reached 0.88.The mean relative error between retrieved LAI and measured LAI in different growing periods was 15%.The trend of rice LAI could be reflected by the retrieved value and GF\|1 WFV data has high ability to distinguish the waters and road network in study area.
  • Comparative Analysis on Three Land Surface Temperature Inversion Algorithm based on Landsat 8 Data over Sanheba Basin
    Shi Xin，Zhou Maichun
    Remote Sensing Technology and Application. 2018, 33(3): 465-475. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0465
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    The Land Surface Temperature (LST) of TIRS10 / Landsat 8 remote sensing data is studied and analyzed by combining the data and related parameters of Sanheba basin，and the LST inversion algorithm are used the Radiative Transfer Equation Method (RTE)，Mono\|Window algorithm (MW) and Single\|Channel Method (SC).The parameters of the MW algorithm are corrected.The LST gray scale and density segmentation graphs，the histogram of LST and the cross validation flank are used to compare the results of the LST inversion algorithm.The results show that the three kinds of algorithms are similar to the linear fitting degree of LST，and the spatial distribution is consistent.The RTE and SC algorithm are close to each other，the average error of algorithm is 0~0.05 K.the LST of MW algorithm is higher than that of the other two algorithms，the average error of algorithm is 0~1.27 K.The LST of different land cover types in this basin is compared，and the inversion results can effectively reflect the details of the surface thermal field structure according to the different land cover types.The LST values obtained by these three algorithms are compared with the MODIS LST product values.The results show that there is a significant correlation between the LST values and the MODIS LST products.In this paper，3 kinds of the LST inversion algorithms are analyzed detailed accurate on TIRS10/Landsat 8 remote sensing data，provide a reference for other thermal infrared satellite data inversion LST algorithm，but also for the subsequent LST improve the accuracy of inversion basis.
  • The Evaluation of Efficiency of Color Metrics in Monitoring  Robiuia  PseudoacaciaPhenology based on RGB Images
    Teng Jiakun，Liu Yu,Ding Mingtao
    Remote Sensing Technology and Application. 2018, 33(3): 476-485. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0476
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    The widely application of digital camera，especially the appearance of time\|lapse camera，inspired the monitoring vegetation seasonal dynamic using time\|series RGB imagery.Extract critical temporal stage of vegetation dynamic is the most useful aspect.Color index including Green Excess Index(ExG)，Green Chromatic Coordinate(Gcc)，Green Red Vegetation Index(GRVI)，and Hue based on HSL(Hue) are the most widely used metrics.However，their efficiency for specific plants may differ.In this study，the efficiency of four color indices mentioned above were tested taking RGB imagery of Robiuia Pseudoacacia as data source.The critical timing point of plant growth and senescence reflected by greenness of leaves were used to pick out the most efficiency color index.The results showed that the best SOS(Start of the Seasonality)，EOS(End of the Seasonality) can be extracted using ExG following the
  • Spatio-temporal Analysis of Vegetation Phenology with Multiple Methods  over the Tibetan Plateau based on MODIS NDVI Data
    Zhou Yuke，Liu Jianwen
    Remote Sensing Technology and Application. 2018, 33(3): 486-498. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0486
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    In the context of global climate change，vegetation phenology analysis based on remote sensing has become an critical method for studying the characteristics of physical and physiological changes of vegetation.This paper uses the MODIS NDVI time\|series data of 96 meteorological stations over the Tibetan Plateau during 2000\|2014 to explore the development trend of vegetation phenological and geographical environment factors of each meteorological station，typical vegetation coverage and the whole plateau region.Firstly，using three cubic spline function method (Spline)，double logistic function method(D\|L)and singular spectrum analysis (SSA)，NDVI time\|series data is reconstructed，then using the derivative method (Der)and threshold method (Trs)，the key parameters of phenological information is extracted，after that differences and application conditions between the six methods are analyzed and compared.Secondly，using M\|K test trend analysis method，the phenological development trend of each site and area were calculated，the relationship between phenological development trend and altitude，precipitation，temperature is studied.Finally，by the Growing season length(GSL)obtained by temperature threshold method，LOS is compared and verified.in grassland and forest land cover types，SSA，Spline，D\|L combined with threshold method to get the Start of Season(SOS)，end of season(EOS)，Length of season (SOS)respectively is a good combination strategy.(2)The spatial differences of various phenological parameters extracted by different methods are large，and the trend is relatively consistent at small scales.Southeast humid and semi\|humid shrub steppe region and northwestern desert steppe in the Tibetan Plateau，SOS and EOS delayed，but LOS prolonged；southwestern humid region，SOS and EOS delayed，LOS shortened；widely distributed grassland，the phenological parameters did not show significant tendency.(3)Temperature is related to the development trend of phenological parameters.With temperature increasing，the phenomena of SOS advance，EOS lag are presented.Because of the complexity of the plateau landform and climate，there was no significant relationship between phenological development trend for most of the site with the altitude and precipitation，only a few sites have strong correlation，the correlation between GSL and LOS also showed similar characteristics.For remote sensing based phonological analyses，our study identify there is no method existing here that is a adaptive across all the Tibetan Plateau.in addition，at point scale the phenological parameters do not represent a significant earlier or later trend.
  • Fully Polarimetric SAR Ship Detection based onPCDM Shannon Entropy#br#
    Zhang Cheng，Zhang Hong，Wang Chao
    Remote Sensing Technology and Application. 2018, 33(3): 499-507. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0499
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    Polarimetric Synthetic Aperture Radar（PolSAR）data contains rich polarization information about the scattering properties of ground objects，having beenwidely used in maritime monitoring and objects detection.The polarization reaction differences between ship targets and sea clutters are analyzed.A ship detection method using the Shannon entropy of the Polarimetric Covariance Difference Matrix (PCDM) is proposed in this paper，which is applied to fully polarimetric SAR images.To enhance the contrast between the ship targets and sea background，the PCDM is generated by calculating the elemental differences between the polarimetric covariance matrix at each pixel and its neighbors.Then the Shannon entropy of SAR images are extracted on the basis of the Shannon entropy calculation formula，and the character difference between the ships and background in the Shannon entropy map is presented for ship detection.The false alarms in the detection result caused by the azimuth ambiguities are removed，based on the displacement distance and energy ratio relationship，between the target and azimuth ambiguity.The Radarsat\|2 Fine Quad data and the Chinese GF\|3 Quad\|Polarimetric Stripmap Ⅰ data are used，to verify the effectiveness of the proposed method，and the SPAN method，HV channel image and polarimetric whitening filter (PWF) method are applied for comparison.The detection and comparison results indicate that the proposed method is able to effectively enhance the ship\|sea contrast，and has higher detection accuracy.
  • Snow Cover Variation and Meteorological Factor Researchin Yarlung Zangbo Basin of Tibet from 2002 to 2015
    Labazhuoma，Cizhen
    Remote Sensing Technology and Application. 2018, 33(3): 508-519. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0508〖
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    The snow cover variation in Yarlung Zangbo from July 2002 to April 2015，as the mother river of Tibet，has been analyzed in this article.The hydrological year is from September of the year to August of the next year.The snow cover variation was studied used by cloudless snow cover data at 500 m and surface temperature from MODIS，digital elevation model (DEM) and meteorological data.The relationship was established between surface temperature from MODIS and major meteorological factors.The results show that the average snow cover in the study area is 3.56× 104km2 in the past 12 years，showing an overall decreasing trend.The area with large snow cover is in the upper and lower reaches of the basin，and the stable and seasonal snow dominate the rural area in the middle reaches and valley basins，with perennial less snow distribution.The stability of snow and perennial snow distribution is more than 4 000 meters above the sea level，which the snow lasts for more than 180 days.And the seasonal snow is mainly less than 4 000 meters above sea level with large variation of snow cover in the area.The inversely relationship is between the air temperature and the area of snow cover，but not significant between the precipitation and the area of snow cover.And the correlation in the surface temperature and the area of snow cover is obvious.In Short，the rising temperature is the main driving factor of snow area reduction in the basin.
  • A Object-oriented Landslide Extraction Method based on Morphological Opening Operation
    Wang Ning，Chen Fang，Yu Bo
    Remote Sensing Technology and Application. 2018, 33(3): 520-529. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0520
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    Landslide is one of the most common geological disasters，and it is of great significance to quickly and accurately obtain the hazard degree and distribution of landslide.By introducing the morphological opening operation and the regional level set algorithm to construct the object\|oriented landslide extraction method，and using the multi\|spectral image of GF\|2 satellite as the data source，the landslide extraction experiment is carried out by using the constructed method and the landslide extraction method based on the pixel in the study area of the Bagmati area in Nepal，and the results of the landslide extraction are analyzed.The experimental results show that the object\|oriented landslide extraction method is more accurate than the extraction method based on the pixel，and the anti\|interference ability of the clouds and snows is stronger than that of the pixel\|based landslide extraction method.
  • Study on Power Tower Extraction and Fast Positioning from Airborne LiDAR Data
    Guo Tao，Shen Ping，Shi Lei
    Remote Sensing Technology and Application. 2018, 33(3): 530-535. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0530
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    Airborne LiDAR has become an important technique for transmission line digitalization，reconstruction and safety inspection.Moreover，accurately and efficiently extracting the position of each tower from massive point clouds is basic and important task for the applications in power industry.In this study，a method was proposed to efficiently extract the point clouds and fast determine the position of power towers using airborne LiDAR data.Firstly，the point clouds of power towers were automatically separated from raw data based on the spatial distribution characteristics of airborne LiDAR data.Secondly，each power tower was efficiently detected using a region\|growing algorithm.Finally，a least square linear fitting method was used to determine the accurate position of each power tower.The new proposed method was applied to several LiDAR data sets in areas with high voltage transmission lines.Results indicated that the integrity of the power towers’ points is up to 91.1%，and the accuracy of center positions is high enough with the medium error of 13.5 cm.Additionally，our study also concluded that the proposed method is robust and applicable even the point density is relatively low.
  • Research on Extraction of Burned Area based on Saliency Enhancement
    Shan Tianchan，Wang Changlin
    Remote Sensing Technology and Application. 2018, 33(3): 536-544. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0536
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    The information of burned area is significant for post disaster assessment，ecosystems protection and restoration.So far，the existing detection methods of burned areas are less practical.Based on the FY\|3C MERSI satellite data，various characteristics of the burned area are fully utilized and a new method of burned area mapping is created through saliency enhancement.Two burned areas in the northwestern United States were selected as research areas.Three burned\|area\|sensitive vegetation indices(NDVI，GEMI and NDVIT) were combined with the saliency features of the images to enhance and extract the burned areas.Visual interpretations are used to evaluate the experimental results of the proposed method，and compared with the results of NBR threshold method.The results show that the kappa coefficient of the saliency enhancement method in the two research areas reaches more than 0.68，0.2 higher than NBR threshold method.Experiments show that the saliency enhancement method for mapping burned area is high，and the influence of vegetation change caused by non\|burned has little influence on it，and the method has a certain stability compared with the NBR threshold method.
  • Integration and Case Analysis of BRDF Model based on Linear Kernel\|driven Model#br#
    Ding Anxin，Jiao Ziti
    Remote Sensing Technology and Application. 2018, 33(3): 545-554. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0545
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    Anisotropic reflectance is the intrinsic characteristic of an object surface.over the past few decades，various BRDF models have been developed for investigating the relationship between the vegetation canopy and reflectance anisotropy.This helps to retrieve biophysical parameters from the anisotropic reflectance patterns of vegetation canopy.In this study，for the purpose of assisting potential users to use these models，and to improve the understanding of the BRDF modeling，several BRDF models that are widely used in the remote sensing community have been integrated with the current version of the MaKeMAT (Multi\|angular Kernel\|driven Model Analysis Tool)，based on the Interactive Data Language (IDL).This work retains all functions of the current version of the MaKeMAT model，meanwhile，adds some new functions by integrating these physical BRDF models.Undoubtedly，this work facilitates the potential users to process BRDF data and make further analysis in their work by operating a simpler visual interface.This helps to build a rapid communication between the kernel\|driven BRDF models and the physical BRDF models.Our initial results show that this model\|integration practice is a valuable reference for potential users to devise a similar technique.Our case study in coupling these physical BRDF models with the kernel\|driven models present a high correlation between them，with the determination of coefficients (R2) reaching 0.899~0.989 in the red and NIR bands.
  • Multi-Observed Block Adjustment for Satellite ImagesWithout Ground Control Points
    Yao Xinghui，You Hongjian
    Remote Sensing Technology and Application. 2018, 33(3): 555-562. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0555
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    In order to ensure the accuracy of target area’s ground information，it is necessary to do geometric correction for remote sensing images.Geometric correction has a great influence on the application of remote sensing images.Traditional geometric correction needs ground control points.However，it is difficult to obtain ground control points in some places，such as abroad，western China and desert.To improve positioning accuracy without ground control points，multi\|overlapping block adjustment model is built.Different from traditional method，error equations are built depending on multi\|projection in image space of a single object.In this way，error equations can converge to more accurate solutions.By adjusting the rational polynomial coefficients of each image，the positioning errors in different directions are compensated to a certain extent.Thus the positioning accuracy of remote sensing images is improved.First，block adjustment model and error compensation model are built with RPC coefficients.Then，conjugate gradient algorithm is used to solve the error equations iteratively.Finally the RPC coefficients are adjusted to improve the accuracy of positioning without ground control points.The ZY\|3 data test shows that multi\|overlapping block adjustment model increase the plane positioning accuracy of remote sensing images from 19.8m to 12.9m and the method can effectively improve the absolute positioning accuracy of remote sensing images.
  • Landsat 8 Data Mountain Shadow Removal based onBrightness Recovering Model
    Lai Riwen，Chi Yufeng，Zhang Zejun
    Remote Sensing Technology and Application. 2018, 33(3): 563-572. https://doi.org/10.11873/j.issn.1004-0323.2018.3.0563
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    Mountain region in remotely sensed imagery are usually covered by shadows，which reduce the accuracy of information extraction.Therefore，in this paper a method based on intensity restoration is putting forward necessarily.First，Shadow Detection (SD) was constructed by the Max function and the band ratio to identify shadows.Thus，mountain shadows were extracted combined with the slope factor and SD，through the grid randomly arranged verification point verification accuracy.Second，the intensity curve model of the shadow area was fitted by ground data of the shadow and the transition rules of pixel intensity from the shadow to non\|shaded area.Third，the intensity restoration model was established by the derivative function of intensity curve to remove shadows.The results of the model on Changting Landsat 8 imagery indicated the extraction accuracy of the mountain shadow was 99.06% and the Kappa coefficient was 98%；According to the cluster analysis，the restoration and non\|shaded samples were the same type；Processed by the intensity restoration model，the average intensity of the shadow was increased by 13%，and the standard deviation was reduced by 80% and the clustering distances was reduced by 96%.respectively，average intensity of the shadow increased by 6.7%，the standard deviation was reduced by 73.7% and the clustering distances was reduced by 88.3% when compared with ATCOR_3，and average intensity of the shadow reduced by 1.8%，the standard deviation was increased by 6.7% and the clustering distances was reduced by 90% when compared with unitary linear restoration model.In the process of removing the mountain shadows，the intensity restoration method is neither replacing the shaded pixels nor interference with non\|shaded pixels and could preserve the spectral and intensity characteristics of shaded pixels better.


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