Retrievalof Agriculture Greenhouse based on GF-2 Remote Sensing Images

Timely and accurately acquisition of the area and spatial distribution of greenhouse in the agricultural regions using remote sensing technique is a novel solution,which would be valuable for the local authorities taking measures to adjust regional agricultural structure and to prevent and control environmental pollution.In this study,the nearest neighbor method based on object\|oriented thought is used to extract greenhouses in Guantao County of Handan City with GF-2 satellite image.The random verification shows that the accuracy of extraction in greenhouses is 95.65%,and the area of the greenhouse is 21.11 km².Since auxiliary facilities around greenhouses were also included in the area of greenhouses issued by local authority,the extraction results need to be revised by calculating the ratio of greenhouse in the greenhouse area.As a result,the final area of greenhouses is 33.68km²with the area accuracy of 87.80% (compared with the official statistics:30 km²).Greenhouses in Guantao County were obviously spatially clustered in some zones along traffic arteries and main rivers,especially around the Zhaizhuang village (about 0.93 km²).Using Chinese high-resolution satellites images to extract information of greenhouses can be effective and feasible with suitable method,and can provide technical support for decision makers to the spatial planning and management of agricultural greenhouse and the supervision and control of agricultural pollution.     

A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.     

A Review of Lake Dynamic Change Researchbased on Remote Sensing

The shrink and expansion of lakes can reflect the regional changes in climate and environment. It has an important significance for further research of the climate change and the sustainable development.The rapid development of remote sensing technology has provided technical support for the dynamic change to real-time monitoring of lakes.This paper discussed the selection of data source,the delineation of lake water,the lake variation trends and causes.Then made a systematic summary of the current situation and progress in the studies on lake change and predicted the trends of lake change research in the future.     

Comparative Study on Thermal Environment between Old Urban Areas and New Urban Areas based on Landsat 8 Remote Sensing Image：A Case Study of Chengdu City

A large number of new urban areas have emerged under the rapid urbanization background in recent years in China,and the characteristics of urban thermal environment have significant changes.To analyze the thermal environment diversities between old urban areas and new urban areas and explore the impacting factors,we chose Chengdu City in Sichuan Province as a typical study area.Key surface parameters,including Land Surface Temperature (LST),building index and vegetation index were derived based on Landsat 8 satellite image acquired on August 13,2014.The comparison study was conducted to analyze the differences of the parameters related to thermal environment changes,and the results indicated that：①Overall,the average surface temperature of the old urban area was higher than that of the new urban area.Regarding the spatial distribution,the central and northern region had higher temperature than the southern region for the old urban areas.In the new urban areas,although high temperature spots can be found in the central west or north,this region generally had a relatively low temperature.②The old urban area emphasized a higher Urban|Heat|Island|Ratio|Index (URI),which revealed the descending trend of surface temperature via analyzing the thermal field profile from the old to the new.This showed that the urban thermal environment effect of the old city was stronger than the new urban area.③“Heat Island Effect” was easy to emerge in those areas with high density urban construction and little urban vegetation coverage,whereas reasonable urban landscape planning and layout would help to perfect the urban thermal environment.The comparison of the thermal environment effects between the new urban areas and the old urban areas shows the new urban planning in Chengdu has positively contributed to improve the thermal environment in the new urban areas,which can provide important reference for the future urban planning.     

Aircraft Auto-detection in Domestic High Resolution Remote SensingImages Using Deep-learning

It is of great significance to automatically detect aircrafts from remote sensing imagery to get their locations.However,due to aircraft posture variance,complicated background and incomplete outlines,it is challenging to achieve a high aircraft detection accuracy.Traditional aircraft detection methods are usually based on hand\|crafted features and machine learning based classifiers,which is not robust enough for the translation and rotation variations.To tackle the above issues,this paper introduces deep convolutional neural network and the strategy of transfer learning to detect aircrafts from Chinses domestic satellite remote sensing images.Specifically,this paper first constructs an aircraft sample database,which consists aircrafts of different sizes and poses.Afterwards,YOLO V2 trained with natural images is utilized as the detection model and is further fine\|tuned with aircraft samples to increase the robustness and performance.Experiments were done on the Shanghai Pudong airport from Chinese GF\|2 remote sensing data.Experimental results showed a good performance with a recall of 92.25% and a precision of 94.93%.It is indicated that deep learning together with model transfer can get a high aircraft detection accuracy with limited training samples.The method in this paper can be generalized to other land object detection problems which shows a good promotional value.     

Effects of Different Spatial Resolution of Remote SensingImages on Estimation Accuracy of Urban Building Height

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.     

Landsat 8 Data Mountain Shadow Removal based onBrightness Recovering Model

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.;     

A Remote Sensing Classification Methods of Coastal Wetlands Considering the Distance to Main Water

Coastal wetlands is complex,"different objects with the same spectrum" is serious in the remote sensing image,so the classification accuracy only based on spectral information is low.For this issue,based on the coastal zone wetland's spatial distribution rule,this paper established two kinds of distance layers,distance to coastline layer and distance to river layer,which applied maximum likelihood method and decision tree method,and developed a coastal wetland remote sensing information extraction methods,taking Sheyang County,Jiangsu Province for example.The developed methods highly improved the classification accuracy with the overall classification accuracy of 81.5%,and Kappa of 0.79.The maximum likelihood supervised classification method classification accuracy was lower with overall classification accuracy of 62.3%,and Kappa of 0.60.     

Classification of Vegetation Coverage of Wetland Landscape based on Remote Sensing in the Coastal Area of Jiangsu Province

The Vegetation Coverage Estimation Model (VCEM) was established based on the classification of coastal wetlands by remote sensing technology and the improved dimidiate pixel model as well as the Normalized difference vegetation index (NDVI).The VCEM was used to calculate vegetation coverage (Fc) and grade level for different wetland types in the study area of Yancheng,Jiangsu Province.The relationship between wetland vegetation coverage and their types was analyzed further by overlaying the coverage classification map and wetland landscape map.The results show that the zonal distribution of plant community of the coastal wetland was notable.And the vegetation coverage level varies with different types of wetland.The reed marshes tend to grow at the high vegetation coverage zone,which account for 67.45% of the total area of reed marshes,and the medium vegetation coverage zone account for 23.61%,and the low vegetation coverage zone only account for 8.94%.Most the Suaeda salsa marshes were in the medium vegetation coverage zone,which account for 81.14% of its total area,and 3.36% and 16.06% were distributed at the high vegetation coverage zone and the low vegetation coverage zone respectively.Most of the Spartina alterniflora marshes were distributed at the high vegetation coverage zone,accounting of 83.47% of its total area,and only 15.07% at the medium vegetation coverage zone,1.74% at the low vegetation coverage zone.In addition,most high vegetation coverage areas of wetlands were distributed at the middle location of each vegetation zone,and the Transition zone for each wetland type was usually with lower vegetation coverage level.     

Error Sources and Post Processing Method for Land Use/cover Change Estimation of Dongguan Citybased on Landsat Remote Sensing Imagery with SVM

Urbanization is the world developing trend in the past century,which significantly changed the land use/cover of the urbanized area,and caused a series negative impacts,such as water shortage,flood increase,environment pollution,ecosystem degradation.How to estimate the land use/cover change more accurately has the prerequisite of studying the urbanization processes and its impacts,and is the research hot and challenge of the remote sensing and application communities.Dongguan city expressed the rapidest urbanization in China since China’s reform and opening door,and transferred from an agriculture county to a modern international metropolitan in less than 30 years,which has made a miracle in the world urbanization process.To prepare a high accuracy land use/cover change dataset for studying Dongguan’s urbanization process and its impacts,this paper first estimated the land use/cover change dataset by employing Support Vector Machine auto\|classification algorithm based on 12 Landsat remote sensing imageries from 1987 to 2015 at an average interval of 3 year.Then the error sources is analyzed by comparing the results estimated by using auto\|classification algorithm and visual interpretation,and a post data processing algorithm is proposed for refining the auto\|classification results.The final dataset of land use/cover change of Dongguan City is produced with the above method with an average accuracy of 86.87% and a Kappa coefficient of 0.83,which implies this product has a very good accuracy for analyzing the urbanization process of Dongguan city and its impacts.     

Analysis of Landscape Characteristics of Urban Heat /Sink Islandin Hangzhou based on ASTER Remote Sensing Imagery

Rapid urbanization has significant contributions to the Surface Urban Heat Island (SUHI).Analyzing the SUHI distribution and its impact factors using remote sensing data has received increasing attentions in the past decades,whereas few study has investigated that of the surface Urban Heat Sink Island (SUHI).The paper selects Hangzhou metropolis as a case study to explore SUHI/SUHS spatial patterns and its causes.We first retrieve the Land Surface Temperature (LST) using ASTER thermal infrared remote sensing imagery and extract the region of SUHI/SUHS using the Mean\|Standard deviation method.Landsat8 OLI data is used to classify land use and extract both impervious surface and vegetation information.After that,different landscape patterns within SUHI/SUHS area are analyzed and quantified by using several selected landscape index.The largest impact factors in SUHI/SUHS areas are identified.Finally,we analyze the spatial characteristics of LST using the spatial gradient analysis method,and reveal its relationship with vegetation and impervious surface.The results show that：(1) a large landscape pattern difference exists within SUHI/SUHS area;the impervious surface has the greatest impact on LST of the SUHI area,whereas the vegetation has more obviously cooling effect on LST of the SUHS area than the water body;(2) with the increasing distance from the city center,the same trend was found between the mean LST values and the impervious surface density (positive correlations),whereas the opposite trend between the mean LST values and the vegetation density (negative correlations).And the warming effect of impervious surface is greater than the cooling effect of vegetation in Hangzhou.     

The Remote Sensing of Sargassum on Daya Bay

There are a lots of sargassum growing on the sea area of Daya Bay. The sargassum is a type of big algae with body length about 1～2 meters and the longest about 5～6 meters. The sargassum begins to grow in Fall of every year, breaks in April or May of the next year and floats away with current. It is the major brigade material to the cooling system pipe of Daya Bay Nuclear Power Station. So, it's needed to investigate its culture regularity, distribution, productions and floating quantity toward the cooling system pipe on the Bay. As the Bay area is very wide, it's quite difficult to determine the distribution range and the total production only depended upon on site investigation. Remote sensing investigation, is a very effective method.     

Convolutional Neural Network for RemoteSensing Plant Cover Extracting

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.     

Tectonic Interpretation and Analysis based on Multi-source Remote Sensing Data：A Case Study of JitaiRiver in Chayi，Tibet

Linear Structures and Ring Structures are of great important to distinguish and analyze faults,folds and magmatic emplacement on the surface.Extracting linear structures from multi-source remote sensing data with the approach of Human-Computer-Interaction can understand the overall and individual geometrical characteristics of Linear Structures and Ring Structures objectively and comprehensively.Taking Jitai river as an example,three sets of Linear Structures with characteristic of clustering and abundant Ring Structures were extracted in working area based on remote sensing data from Google Earth,Landsat 8/OLI,ASTER GDEM and high-resolution DEM.The results of the analysis show that the working area is in a dextral shear zone with northwest direction and the southwest structure of Jitai River is still in a relatively active stage,which may be an unstable area of the engineering geology and the prone areas of geological disasters.     

Model Building and Application for Monitoring and Assessing the Aging Conditions of Asphalt Road Pavementsbased on Remote Sensing Data

To meet the demands in monitoring the health conditions of road pavements over a relatively large area,by means of derivative and continuum removal approaches this study analyzes the spectral features of asphalt road pavements aging degrees based on the field measurements of pavement spectra.Distinct spectral features of new and aged asphalt road pavements were observed in the wavelength regions of 400~680 nm and 860~970 nm.After that,a WorldView-2 image in Liangxiang area,Fangshan district,Beijing City were captured and the corresponding bands were used to create a Multiplication Aging Index (MAI) to reflect the aging conditions of asphalt road pavements.Comparison between the MAI and in-situ measurements of the spectra and aging conditions of the road pavements in the study area was performed,and statistical analysis was also conducted based on the Munsell brightness values collected in the field investigation.Through the contrast,the aging condition from MAI has good relevance to the in-situ measurements.Results indicate that the proposed MAI index can reflect the aging conditions well and is further used to monitor the pavement quality of the 14 road pavements in the study area.According to the evaluation,six roads in the study area need road maintenance.The research can offer a new technology for road management departments to make their road maintenance plans.     

Radiometric Consistency between Landsat 8 OLIand Sentinel-2 MSI Imagery in Mountainous Terrain

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.     

PlantingInformation Extraction of Winter Wheatand Rape based on GF-1 Images

Successive emission of high resolution satellite has created new opportunities for the application of domestic high resolution remote sensing data.In order to explore the feasibility of GF data in the field of small and medium scale crop remote sensing monitoring and to establish a suitable technical system,with Yangzhou as an example,using decision tree model and object oriented classification method to research the feasibilityon crop planting information extraction of GF wide field viewdata.And explore the method to improve the accuracy.The results showed that,sub\|regionpretreatmentcan reduce the adverse effects of crop spatial distribution on the extraction of the planting area.The overall accuracy of winter wheat was 97%,the Kappa coefficient was 0.93;the overall accuracy of rape was 96%,the Kappa coefficient was 0.84.Research shows thatdomestic GF\|1 WFV images can be applied to the crop planting informationextraction,and toprovide an important reference and decision support for adjusting crop spatial and optimizing management of gain producing areas.     

Research on Hyperspectral Imagery Land Cover ClassificationMethod based on AdaBoost Improved Random Forest

In order to improve the classification accuracy of hyperspectral images,a new weighted random forest method based on AdaBoost is proposed.In this method,the concept of sample weight is introduced,and then the weight of each sample will be adjusted according to whether the sample is correctly classified.Those misclassified samples will be given higher weight value,to attract more attention of the classifier to improve the classification.Furthermore,the method gives the voting weight to every basic classifier according to their classification error rate.The basic classifier with higher classification accuracy will obtain larger voting weight.Two sets of Hyperspectral data(The CASI Hyperspectral Data acquired in Heihe region and CHRIS Hyperspectral Data acquired in the Yellow River Estuary) are used to verify the validity of the method.The results show that the weighted random forest has a better performance than the equal weight random forest and the SVM method in the overall classification accuracy,the average classification accuracy and the Kappa coefficient,which proves the efficiency of the proposed method.     

Remote Sensing Prospecting in High Vegetation Coverage Area——A Case Study of the in Datuanbao Copper Ore Deposit

This article described the way to extract information of copper and lead anomaly in high vegetation coverage area through a case study from the Datuanbao copper ore deposit and its environs,Jiangcheng,Yunnan Province.Principal component analysis,false color composite,stretch Histogram and supervise classification etc were analyzed in the remote sensing images using ENVI4.6 software.It is suggested that the linear structure,which is delineated by convolution filter and morphological analysis method,is characterized by statistical self-similarity and fractal geometry.It is found that the high value of fractal dimension is roughly coincident with the copper and lead anomaly of vegetation by the comparison between contour maps of plant geochemical anomaly and fractal dimensions,which is calculated by box-counting.As a matter of fact,80 percent of copper and lead deposits or occurrences are located in areas of high value of fractal dimension and vegetation anomaly.By the comprehensive analysis of stratigraphy,igneous rocks,copper and lead mineralizations,fractal dimension of linear structures and vegetation anomaly from remote sensing,nine exploration targets with three levels of probabilities are figured out.