20 February 2018, Volume 33 Issue 1
    

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  • Wang Guangzhen,Wang Jingpu,Zou Xueyong,Han Liu,Zong Min
    Remote Sensing Technology and Application. 2018, 33(1): 1-9. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0001
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    The quantitative estimation of fractional cover of photosynthetic vegetation(f PV),non-photosynthetic vegetation(f NPV),and bare soil(f BS) is critical for grassland ecosystem carbon storage,vegetation productivity,soil erosion and wildfire monitoring.The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands,forests,grasslands savannah,and shrublands using remote sensing.This paper reviews the research progress in estimating f NPV using hyperspectral and multisspcetral remote sensing data,and hightlights discusses the theoretical bases of PV,NPV and BS spectral characteristics.based on the existing methods for estimating f NPV,this article groupd into two categories:empirical relationship between spectral index and NPV cover,and Spectral mixture analysis.Meanwhile,also discuss applications.of hyperspectral and multisspcetral remote sensing data.Finally,the existential problems and research trends for NPV estimation are analyzed.
  • Wan Ling,You Hongjian,Cheng Yuebing,Lu Xiaojun
    Remote Sensing Technology and Application. 2018, 33(1): 10-24. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0010
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    Image segmentation is an indispensable part for synthetic aperture radar (SAR) image automatic interpretation.Segmentation results with high-quality are the guarantee of SAR image applications.In addition,owing to the development of SAR sensors,the segmentation task based on SAR image has been widely concerned in recent years.However,compared with the optical images,the unique properties of SAR images lead to great challenge in SAR image segmentation.With the development of pattern recognition,machine learning,remote sensing technology and other related techniques,SAR image segmentation has made great progress.This paper reviews the progress of SAR image segmentation,and then puts its emphasis on the summary of the widely used algorithms:FCM,MRF,statistical model,region information,level set,multi-scale and deep learning,etc.Finally,several viewpoints for the future research of SAR image segmentation are proposed.
  • Tang Yuming,Deng Ruru,Liu Yongming,Xiong Longhai
    Remote Sensing Technology and Application. 2018, 33(1): 25-34. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0025
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    Air aerosol pollution has become increasingly serious and become the focus of atmospheric research with the development of urban industrialization.Remote sensing technology is widely used in atmospheric research as a means of scientific,rapid and large-scale monitoring.The main content of remote sensing for atmospheric aerosol retrieval including Aerosol Optical Depth (AOD) of aerosol,aerosol concentration aerosol particle size distribution and air pollution are analyzed based on the atmospheric radiation transmission theory.Main progress around of the world in areas of atmospheric aerosol research by remote sensing techniques are introduced,especially the advantages and disadvantages of the different aerosol retrieval algorithms.At last,some existing problems and the trend of remote sensing for atmospheric aerosol retrieval are discussed.
  • Zhang Shuai,Shi Chunxiang,Liang Xiao,Jia Binghao,Wu Jie
    Remote Sensing Technology and Application. 2018, 33(1): 35-46. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0035
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    Due to the unique function that snow played in modulating energy and water exchanges in climate and hydrology system,it is important to estimate snow distribution and produce high quality products for short-term climate prediction and water resources management.National Satellite Meteorological Center publics FY-3 snow cover fraction product since 2009.It is necessary to evaluate the snow cover fraction product in order to verify the precision of retrieval algorithms and provide an objective evidences for climate studies.based on MODIS MOD10C1(MYD10C1) Global Daily Snow Cover Dataset,we carries out an evaluate of FY-3 snow cover fraction product from 2010 to 2014 based on five examine indexes,and analyses the bias distribution of snow cover fraction product in different time scales further.It is concluded that FY-3 snow product is a better time space consistency with MODIS MOD10C1(MYD10C1).For example,the consistency of two products is better in snow accumulation period,while it is reducing influenced by cloud detectionin snow melting time.At the same time,bias of snow cover fraction products have obviously changes in inter-annual time,seasonal and monthly.compares to MODIS products,FY-3 snow product is higher in North China,but it coverts to lower in whole China since 2012.Bias of two products decreases from snow accumulation period to snow melt period.In monthly time scale,North eastern China and north of Sinkiang area is sensitive area of snow variation.Bias is more stable because of Tibet Plateau is influenced by topography and covered with snow all the year.
  • Hu Rui,Xiao Pengfeng,Feng Xuezhi,Zhang Xueliang
    Remote Sensing Technology and Application. 2018, 33(1): 47-54. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0047
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    Radiometric quality of satellite’s image is closely related to its monitoring capability.GF-1 is the first satellite of China’s high-resolution earth observation system and has an important application value in monitoring mountain snow.In this paper,we compare the radiometric quality of GF-1 satellite image to the similar overseas high-resolution satellite SPOT-6 and RapidEye in snow-covered area of Manasi River Basin.The results could provide some advices for future GF series satellites’ development in China.Using the image statistical features,average gradient,information entropy,and SNR,the radiometric quality of the three satellite images is compared.The results show that the GF-1’s image statistical features and average gradient has the best performance.However,the information entropy has a certain gap compared with the other two satellites due to the depth of radiation.GF-1 partially supersaturated during snow imaging,resulting in part of region having zero value.After removing these outliers,GF-1 image’s SNR is re-estimated and found to be significantly superior to other satellites in the blue,red,and panchromatic bands,whereas,the green and near-infrared bands still have a certain gap compared to SPOT-6,but similar to RapidEye.Overall,the radiometric quality of GF-1 image in the mountainous snow-covered area has basically reached the level of similar satellites in the world and some indicators are even better than other satellite images.Limited to the depth of radiation and the range of radiation energy accepted by the sensor,an over-saturation phenomenon will appear in some snow-covered regions,the follow-up can be further improved in this regard.
  • Liu Jiange,Mu Dejun
    Remote Sensing Technology and Application. 2018, 33(1): 55-60. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0055
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    The Marginal Ice Zone (MIZ) consists of different ice types,which makes it very dynamic.The dynamic features of sea ice in SAR imagery show as numerous curves in random orientations and scales.According to these curve features,the paper use middle scales coefficients of the curvelet transform which gives an optimal sparse representation of singularities along smooth curves at multi-scale and multi-direction to design a dynamic feature extraction method in SAR imagery.The feature is related to the mean and GLCM energy of curvelet coefficients magnitude and its neighborhood.The MIZ getting from the proposed feature has an obvious improvement of accuracy comparing with the MIZ getting from the SIC data.The results demonstrate that it is an effective way to extract dynamic feature of sea ice.It can be used as the first step of the detection of MIZ,also used as an effective parameter in sea ice analysis model and environment prediction model.
  • Wang Jikun,Chen Zhenghua,Yu Kefu,Huang Rongyong,Wang Yinghui
    Remote Sensing Technology and Application. 2018, 33(1): 61-67. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0061
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    Water depth inversion is of great important to coral reefs’ protection and engineering construction of the coral reef region.As the correlation between the remote sensing radiance and the water depth is a very well,so the band ratio transform and the linear combination transform were adopted to the water depth inversion around coral reefs.Based on the complex topography of coral reefs,these two algorithms were optimized for the water depth inversion.Finally,our experimental results show that the improved ratio transform was suitable for retrieving water depths among 3~5 m and 5~10 m.While none of the models that mentioned in this paper performed well in water depth of 5~10 m,the preliminary conclusion was that coral reefs’ special topographic caused it.
  • Ma Lina,Li Qing,Jiang Sulin
    Remote Sensing Technology and Application. 2018, 33(1): 68-77. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0068
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    In order to estimate the working state of the radiometry channels by using “clear sky” samples and to find out the correction relationship for each channel to further investigate the “cloud” samples,we firstly make a consistency analysis of observed brightness temperature data (T BM) obtained from Microwave Radiometer located in Conghua district of Guangzhou province and the simulated brightness temperature data (T BC) calculated by using NCEP atmospheric profile data based on the radiative transfer equation.Then,the “clear sky” samples and "cloud" samples (including few ambiguous data) are identified using multi-channel brightness temperature difference threshold method.Finally,the linear correction for the "clear sky" samples based on the regression analysis are used to establish the correction relationship between T BM and T BC to estimate the working state of the ground-based microwave radiometer.According to the comparison of the time series between the cloud bottom height data of radiometer and the corrected brightness temperature,it is proved that the multi-channel brightness temperature difference threshold method is good to identify the clear sky.The corrected “clear sky” samples can be directly used in the subsequent air temperature and humidity inversion process,and the identified “cloud” samples (including rain samples) provide a base for the subsequent cloud parameter inverse algorithm and the cloud effect correction of the brightness temperature data,which can be further used in the subsequent inverse of the air temperature and humidity for all the samples.


  • Bai Yu,Meng Zhiguo,Zhao Kai
    Remote Sensing Technology and Application. 2018, 33(1): 78-87. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0078
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    Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP(Soil Moisture and Active and Passive) and SMOS(Soil Moisture and Ocean Salinity)passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained:(1)Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;(2)Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; (3)SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.
  • Song Tingting,Fu Xiuli,Chen Yu,Wei Yongming,Wang Qinjun,Cheng Xianfeng
    Remote Sensing Technology and Application. 2018, 33(1): 88-95. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0088
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    As a product of the development of modern industry and mining industry,heavy metal Zn pollution has gradually invaded the daily production and life of human beings,which is harmful to our physical and mental health.In dealing with large-scale soil environmental monitoring.The traditional heavy metal monitoring method is time-consuming and laborious.Due to its characteristics of high speed,high speed and high efficiency,remote sensing technology has become an important tool for environmental monitoring in the new era.This study takes Yunnan Gejiu mining area as a typical area,collecting sample in field soil and measurement of soil sample spectra and Zn content.Then the band transform method based on the multiplicative transformation was proposed to enhance product conversion relationship between Zn elements and spectral sensitive bands,using the established prediction model and optimal Zn content based on ASTER images to carry out pollution mapping.Research shows that:①the maximum correlation band of Zn elements is the B515 band,close to the absorption peak of sphalerite and smithsonite zinc containing minerals,is an important band of zinc element inversion of soil;②the spectral multiplicative transformation can highlight the sensitive bands of Zn elements,and retain the most sensitive information of the original soil;③in the hypersecretion inversion model of soil zinc content in the study area,the precision of the model established by partial least squares(R=0.90)is the highest(R=0.70);④The inversion results based on ASTER images show that there is a significant correlation between soil Zn pollution and mining activities(Verification accuracy of map R=0.694).The results of this study can provide the basis and technical support for remote sensing quantitative inversion of heavy metal content and large-scale environmental pollution monitoring.
  • Zhou Ziyong
    Remote Sensing Technology and Application. 2018, 33(1): 96-102. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0096
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    Super resolution (SR) of remote sensing images is significant for improving accuracy of target identification and for image fusing.Conventional fusion-based methods inevitably result in distortion of spectral information,a feasible solution to the problem is the single-image based super resolution.In this work,we proposed a single-image based approach to super resolution of multiband remote sensing images.The method combines the EMD (Empirical Mode Decomposition),compressed sensing and PCA to dictionary learning and super resolution reconstruction of remote sensing color image.First,the original image is decomposed into a series of IMFs(Intrinsic Mode Function) according to their frequency component by using EMD,and the super resolution is implemented only on IMF1,which includes high-frequency component;then the K-SVD algorithm is used to learn and obtain overcomplete dictionaries,and the MOP (Orthogonal Matching Pursuit) algorithm is used to reconstruct the IMF1;Finally,the up-scaled IMF1 is combined with other IMFs to acquire the super resolution of original image.For a multiband image reconstruction,a PCA transform is first implemented on multiband image,and the PC1 is adopted for learning to get overcomplete dictionaries,the obtained dictionaries is then used to super-resolution reconstruction of each multi-spectral band.The Geoeye-1 panchromatic and multi-spectral images are used as experimental data to demonstrate the effectiveness of the proposed algorithm.The results show that the proposed method is workable to exhibit the detail within the images.
  • Zhou Weifeng,Cao Li,Li Xiaoshu,Cheng Tianfei
    Remote Sensing Technology and Application. 2018, 33(1): 103-109. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0103
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    Image fusion is one of the most important steps in remote sensing information extraction.To select the appropriate fusion method is the crucial link.In this paper,Xiangshan Port in Zhejiang Province is the study area,and the oyster culture is the observation target.The satellite of WorldView-2 multispectral and panchromatic images were used to detect the distribution of the coastal oyster farming.The different five fusion methods,such as Principal Component Analysis (PCA),Gram-Schmidt(GS),NNDiffuse Pan Sharpening,Brovey Transform and Wavelet Transform,were evaluated by two of subjective qualitative and objective quantitative aspects.We compared the fused images with the original image using six kinds of statistical parameters including mean,standard deviation,entropy,average grads,correlation coefficient and spectral distortion to evaluate the images’ fusion performance.The results indicate that,for the characteristics of coastal oyster farming,the fusion image by principal component analysis method not only preserves detail spatial texture information but also maintains the spectral character well.The method of PCA is the most suitable fusion method for remote sensing applications in coastal oyster culture with WorldView-2 images.The fusion effect of GS is second to PCA,which can be used as an alternate method for fusion applications.NNDiffuse Pan Sharpening,Wavelet transform and Brovey transform are inappropriate for the identification and extraction of oyster culture floating raft.
  • Liu Huijun,Su Hongjun,Zhao Bo
    Remote Sensing Technology and Application. 2018, 33(1): 110-118. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0110
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    The utilization of hyperspectral remote sensing image is mainly based on the spectral information,and the spatial information is always be ignored.To solve this problem,a novel hyperspectral multiple features optimization approach based on improved firefly algorithm is presented.Firstly,four spatial features,the local statistical features,gray level co-occurrence matrix features,Gabor filtering features and morphological features of hyperspectral remote sensing image are extracted,and some spectral bands are selected and then combined with these spatial features,and the feature set is constructed.Then,the firefly algorithm is used to optimize the extracted features.In view of the slow convergence speed of firefly algorithm,we use the random inertia weight from particle swarm optimization algorithm to modifiy the location update formula of firefly algorithm,and JM(Jeffreys-Matusita)distance and Fisher Ratio are used as the objective function.Two urban hyperspectral datasets are used for performance evaluation,and the classification results derived from spectral information and spectral-spatial information are compared.The experiments show that random inertia weight can improve the speed of FA-based feature selection algorithm,the performance with multiple features is better than that of spectral information for urban land cover classification,The statistical results of the two sets of experimental data indicate that the selected number of morphological features are the most in the four spatial features.The local statistical features and morphological features are more helpful to the classification of hyperspectral remote sensing images than GLCM and Gabor features.
  • Xiong Wei,Xu Yongli,Yao Libo,Cui Yaqi
    Remote Sensing Technology and Application. 2018, 33(1): 119-127. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0119
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    The characteristics of ocean background and target in the high resolution synthetic aperture radar (SAR) images are analyzed.Aiming at the requirements of ship detection in high-resolution synthetic aperture radar (SAR) image,the detection accuracy,intelligence level,real-time and processing efficiency,we put forward a high resolution SAR images ship detection algorithm based on support vector machine.The algorithm designs a pre-training support vector machine (SVM) classifier and complete the screening of the ship target block area,then the algorithm of optimal entropy thresholds proposed by Kapur,Sahoo,Wong(KSW) will be used on the target area selected for fine detection of ship targets.In this paper,several commercial satellite data,such as TerraSAR-X,are used to verify the experiment.Comparing with the classical CFAR detection algorithm,Experimental results show that the algorithm can improve the false alarm caused by the speckle noise and ocean clutter background inhomogeneity.At the same time,the detection speed is also increased by 20% to 35%.
  • Wu Di,Shi Wenzhong,Gao Lipeng,Zhang Hua,He Pengfei
    Remote Sensing Technology and Application. 2018, 33(1): 128-135. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0128
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    This paper proposed a new method which combines the airborne LiDAR data with aerial image to extract Rolling Stones on mountainous.Firstly,the aerial image is processed with multi-scale segmentation to get segmentation objects,and the LiDAR data are processed by classification,interpolation,difference for elevation information.Then compute the segmentation object based on visible-band difference vegetation index to remove the interference of vegetation information,and the nonvegetated segmentation objects are obtained.In order to effectively use the shadow,this paper put forward the normalized difference shadow index and use threshold segmentation to get shadow object.And then the automatic extraction algorithm based on the shadow and elevation information is used to preliminary obtain the rolling stones information.Finally,The height threshold filtering is set according to the actual demand to get the final rolling information.This paper took a certain area of Hong Kong aviation image and LiDAR data as experimental data to validate the proposed method.The results show that the method can well extract the Rolling Stones and effectivly distinguish the exposed bedrock,roads and similar spectral information of ground objects as Rolling Stones.The extraction accuracy of Rolling Stones is above 88% which basically satisfies the needs of rockfall in lands department.
  • Liang Qianqian,Zhang Hande,Sun Genyun,Wang Peng
    Remote Sensing Technology and Application. 2018, 33(1): 136-142. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0136
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    Water regions extraction is of great significance to monitoring,research,planning and development of coastal zones.The conventional method of water regions extraction based on remote sensing image often has poor accuracy in coast because there are large differences among spectral character of different water regions which often change over time as well.To solve this problem,the elevation,intensity and point-density information from the highly accurate 3D mass points of airborne LiDAR was exploited to extract water regions.Firstly,a part of water points were extracted by the characteristics of low density,and then were constrained by the elevation and intensity threshold which came from Statistical table.Secondly,A triangulation network surface model was established based on the water points got from the previous step to describe the elevation trend of water surface.Lastly,all the points which near or behind the surface model was extracted as water points.The result of the accuracy evaluation indicates that the recogniton accuracy of water points is more than 91%.
  • Jiang Dong,Chen Shuai,Ding Fangyu,Fu Jingying,Hao Mengmeng
    Remote Sensing Technology and Application. 2018, 33(1): 143-150. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0143
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    Remote sensing is the main means of extracting land cover types,which has important significance for monitoring land use change and developing national policies.Object-based classification methods can provide higher accuracy data than pixel-based methods by using spectral,shape and texture information.In this study,we choose GF-1 satellite’s imagery and proposed a method which can automatically calculate the optimal segmentation scale.The object-based methods for classifying four typical land cover types are compared using multi-scale segmentation and three supervised machine learning algorithms.The relationship between the accuracy of classification results and the training sample proportion is analyzed and the result shows that object-based methods can achieve higher classification results in the case of small training sample ratio,overall accuracies are higher than 94%.Overall,the classification accuracy of support vector machine is higher than that of neural network and decision tree during the process of object-oriented classification.
  • Tian Deyu,Zhang Yaonan,Zhao Guohui,Han Liqin
    Remote Sensing Technology and Application. 2018, 33(1): 151-157. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0151
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    The key point of the state-of-the-art machine learning method to extract land information is to construct the features-vector.The existing methods mainly use the spectral features,texture features of remote sensing images to construct the features-vector,however,this method can only get limited features and requires too much human intervention.In the face of the above problems,this paper builds a convolutional neural network model for mining the deep-level features of multi-band remote sensing images and then extract the greenbelt in the Kubuqi Desert.The model was trained and hyperparameter selection was performed.The performance of the model was evaluated by cross validation and comparative analysis between methods.The experimental results show that the model is of high accuracy and good generalization ability.Finally,the test data set was input into the model to predict land cover classes and to do visualization.The importance of this study is to inspire new thinking of better performance of the green land and even more complex information extraction from remote sensing images.
  • Wang Kai,Zhao Jun,Zhu Guofeng
    Remote Sensing Technology and Application. 2018, 33(1): 158-167. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0158
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    In Northwest China,there are many mixed pixels in the winter wheat area,so the combination of decision tree and mixed pixel decomposition is of great significance to improve the interpretation accuracy.The data source of this result is GF-1 satellite data which excellent in the high temporal resolution and high spatial resolution.Based on the difference about variation characteristics and NDVI value for winter wheat and the other crops in different phase data,we build decision tree to extract winter wheat pixels preliminary.Then selected linear spectral mixture model,further analysis the previous data by mixed pixel decomposition,get the final planting area data more exactly.Compared with the winter wheat samples measurement data,calculate the extraction accuracy eventually.The result shows that the extraction accuracy of winter wheat planting area in the study area was more than 90%,Kappa coefficient is close to 0.8,can reflect the distribution of winter wheat in the region accurately.This study found that the method which combined with decision tree classification and pixel unmixing based on high resolution remote sensing image can extract the winter wheat planting area precisely,This is helpful for the development of crop area remote sensing monitoring.
  • He Yi,Zhou Xiaocheng,Huang Hongyu,Xu Xueqin
    Remote Sensing Technology and Application. 2018, 33(1): 168-176. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0168
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    Tree number is the index that describes the stand density,and extracting tree number in districts is important for researching forest reserve information.This essay investigates Jiangle forest farm in Fujian to study the applicability of maximum algorithm and multiscale segmentation algorithm in extracting tree number.The first step in this dissertation is to use ebee unmanned aerial vehicle remote sensing with fixed wings to get image whose resolution is more than 10cm,and gets orthoimage after processing.Based on it,this essay uses 20 area samples including coniferous forest and broad-leaved forest.The second,the tree number of samples were extracted from maximum algorithm and multiscale segmentation algorithm;Lastly,this essay uses the tree number abstracted fromthe two algorithm and the tree number from visual interpretation statistics to precision analysis.The results show that the tree number of samples was extracted by the two algorithm in the overall accuracy of about 90%.In the coniferous forest plots the tree number of extraction accuracy by local maximum algorithms is better than the broad-leaved forest plots the tree number of extraction accuracy.In the broad-leaved forest plots the tree number of extraction accuracy by multiscale segmentation algorithm is better than the tree number of extraction accuracy by local maximum algorithms.Therefore,this research argues that local maximum value algorithm is more suitable for unmanned aerial vehicle (uav) remote sensing image on coniferous sample tree number of rapid extraction,the multiscale segmentation algorithm is more suitable for unmanned aerial vehicle (uav) remote sensing image on broad-leaved sample tree number of accurate extraction.
  • Zhao Hang,Chen Fang,Zhang Meimei
    Remote Sensing Technology and Application. 2018, 33(1): 177-184. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0177
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    Glacial lake is one of the most important sensitive factors that reflect the global climate change.The accurate extraction of glacial lake information can be used to analyze the status of glacial lake in different period,and to provide the possibility for the analysis of climate change.Focus on the complex spectra characteristics of glacial lake,and considering that the C-V model has a complete theoretical basis,but the huge amount of calculation,so this paper combines the C-V model with the symbolic pressure function and the “global to local” Iterative water extraction algorithm to study the a new method of glacial lake extraction in High Asia.This paper chooses the three typical development region of glacial lake,Himalaya mountains,Southeastern Tibet,Altai mountains.Glacial lake were extracted by using Landsat 8 OLI image,and then this paper do the research of the accuracy assessment of the glacial lake extraction results.The C-V model has better anti noise performance and weak edge segmentation effect,so the experiment obtained good results.The result shows that overall accuracy of glacial lake extraction is 89.86%,and the method can extract the glacial lake’s information rapidly and accurately.
  • Peng Ling,Xu Suning,Mei Junjun,Li Wenjuan
    Remote Sensing Technology and Application. 2018, 33(1): 185-192. https://doi.org/10.11873/j.issn.1004-0323.2018.1.0185
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    Presently,regional earthquake-induced landslides is mainly obtained by field survey and visual interpretation from remote sensing images; but these methods are objective,and time-consuming.In this study,with a main data source of domestic high-resolution remote sensing images from ZY-3 satellite as well as the study area of the Wenchuan earthquake region,objects of multilevel landslides were established using the multi-scale optimum partition method based on in-depth analysis of landslide features.A recognition rule set of multi-dimensional landslides was also built through the combination of topographic features and image features,such as spectrum,texture,and geometry.Additionally,recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes.Through all of the aforementioned efforts,the spatial distribution of the seismic landslide as well as the sliding source area,transport area,and depositional area can be identified intelligently.The analysis results of the experimental area showed a minimum recognition accuracy of 82.97%,with the depositional zone of landslides being the easiest zone to recognize,and the effectiveness of the proposed method as well as ZY-3 data.These findings may provide technical support for regional earthquake-induced landslides investigations and further promote geological hazard application of domestic high-resolution satellites.