With its high spectral resolution,hyperspectral remote sensing has a great potential in obtaining quantitative information of soil components,such as organic matter,mineral substance.Taking Yangkou town Rudong county as a research area,the coherence between soil heavy metal elements Cr,Cu and Ni and soil clay mineral,iron-manganese oxide and carbonate is studied after soil reflectance spectrums are measured and soil components are analyzed by chemistry methods.Soil spectrums are processed by first derivative,inverse-log and continuum removed methods,then the characteristic spectral wavelengths of soil heavy metal elements are obtained.Given that the best quantitative remote sensing models for 3 heavy metal elements are deduced by stepwise regression after the correlation analysis of soil heavy metal elements content and spectral variables.The results show that 3 heavy metal elements have good correlations with spectral variables at wavelength 429 nm,470 nm,490 nm,1 430 nm,2 398 nm and 2 455 nm,the accuracy of the model for first derivative spectral variable is the highest among the models.The study aims that the content of heavy metal elements in the soils can potentially be quantitatively deduced by remote sensing,and would provide a basis for the evaluation and interpretation of heavy metal and environment pollution on a large scale by the hyperspectral remote sensing.
The paper applied the CCD and IRS data of HJ-1-B which was developed by China independently to monitor the forest fire in Wilsons Promontory of Victoria,Australia.Due to the similarity of the imaging condition,the CCD and IRS data can be registered with each other well and then can be fused to enhance surface objects presentation (such as fire intensity and the objects near the fire).In addition,the intensity and the change of the fire can be monitored efficiently and effectively for the wide range and high temporal resolution of HJ-1-B.
Evapotranspiation retrievals in conditions of rugged terrain and arid/semiarid sparse vegetation are always the hotspot in estimation of regional evapotranspiration from remote sensing images.Concerning with the complex characteristics of the loess hilly-gully region,such as undulating topography,un-unique land use/covers and less vegetated land surface,etc,land surface characteristic parameters and flux was retrieved,and the instantaneous vegetation transpiration and soil evaporation was estimated with the TSEB parallel model by using the Landsat TM data for an experimental study site located on the conjuncture area of Shaanxi and Gansu where the terrain surface is very undulating with less vegetation developed,heavy soil and water loss loess plateau.And then the calculated result was merged into daily evapotranspiration,also the spatial pattern of the daily evapotranspiration was analyzed.Actual evapotranspiration of the study area was calculated with the extra resistance method and Penman-Monteith equation to compare with the TSEB parallel model estimated,which suggested that the TSEB parallel model can be used for the accurate estimation of evapotranspiration in loess hilly-gully region.
A method for urban major road extraction from IKONOS imagery is proposed.The texture features of the imagery were first analyzed in three different levels.The first level calculated the Mahalanobis distance between test pixels and training pixels.The second level calculated the Bhattacharyya distance between the distributions of the pixels in the training area and the pixels within a 3×3 window in the test area.The third level employed cooccurrence matrices over the texture cube built around one pixel,and then calculated Bhattacharyya distance.The processed results were thresholded and thinned,respectively.With the assistance of the geometrical characteristic of roads,the three resultant images corresponding to three levels were merged by fuzzy mathematics.A knowledge-based algorithm was used to link the segmented roads.The result was finally optimized by polynomial fitting.The experiment shows that the proposed method can effectively extract the major urban roads from the high-resolution imagery such as IKONOS.
As an important composition of water resources,surface water can reflect the situation of water resources in certain degree.Firstly,using Beijing-1 small satellite data surface water classification standard and its extraction method were studied and surface water resources in Five Basins of Beijing and its surrounding regions in 2007 was detected,secondly its temporal and spatial distributions rules,annual variance characteristics and variance reasons were analyzed with meteorological and hydrological data.The results are as follows: ① Surface water information can be well extracted using Beijing-1 multi-spectral data with panchromatic data.② The surface water change characteristic in Five Basins of Beijing and its surrounding regions in 2007 was related to the average precipitation in 2007,the annual precipitation variance and reservoir regulation and storage and so on.
Based on RS and GIS,Xi’an City area expansion information were extracted from multi\|temporal Landsat data using supervised classification,non-supervised classification and normalized difference barren index(NDBI).The result shows that Xi’an city area increased 1.6 times from 1988 to 2002,the average growth rate is 8.815% and the new areas mainly distribute in the north and the south parts of the city,Based on the multi-angle view,the paper analyzed the expansion driving factors,such as social economy（GDP）,population growth rate,policy and the investment of the basic construct,and the result showed that the key driving factor of urban expansion is social economy（GDP）and population growth rate,which are impacted by policy to a great content.
Soil salinization has severely threatened the inner stability and the sustainable development of oasis in arid areas,so extracting the saline soil information rapidly based on remote sensing and mastering its spatial distribution are of important practical significance.In this article,taking the Kuqa oasis as the example,TM image collected on August 2001 and Radarsat image collected on Mar 2001 were used.The approach of effective remote-sensing information extraction for saline soil was discussed.The mechanism and characteristics of saline soil and other objects was analyzed to find the possibility of extracting saline soil from the background.The analysis was focused that the radar-band as a band to join the six bands of ETM together was taken to perform the principal component transformation to extract the saline soil information.The research shows that K-L-5 is the best band to extract severe saline soil information,TM1 is the best band to differentiate saline soil information with the salt-tolerant vegetation of Hongliu and the mixed water body and vegetation information can be separated by the index of MNDWI and NDVI.Then,based on the analysis,a simple model of decision tree was applied to extracting saline soil information.Finally,the results were checked by statistical accuracy assessment.The results suggest that the model of decision tree was simple and effective and the precision of this approach was very high,so it was an effective method for monitoring saline soil changes in arid area.It also showed that the separability between the saline soils and other classes was increased due to the participation of radar band,and an effective way was provided to extract saline soils information using radar images.
This paper summarizes the study of marine oil spill monitoring at home and abroad,and pointed out the starting point of this study and research.In this study,it has used the NOAA18 data,due to the non-oil spill and oil spill region that the specific heat capacity of the regional differences are reflected in the satellite images.Because the polluted sea surface changed the radiance which is gray-scale value is different.Therefore,the use of oil spill occurred on the region in satellite images showing differences in gray values which reflected the dark area on the image,and then in the satellite images to identify a suspected oil spill on the region,conducting manual interpretation,and further to determine the occurrence of oil spill area.Taking Yantai “Golden Rose” oil spills for example,comprehensive consideration of factors such as hydrology,the scope of oil spill occurred in Yantai area was analyzed.
Based on the reflectance spectrum of oil slick and background seawater,an EMD based approach to feature extraction from hyperspectral data is presented in the work,and a BP neural network is constructed to classify the oil slick and seawater samples.Firstly,the EMD is adopted to decompose the hyperspectral data from 425 nm to 2 390 nm into a series of Intrinsic Mode Functions ( IMFs) ,and the energy of each IMFs is computed,then based on the computed energy,some IMFs energy are selected as inputed parameter of neural networks to classify the oil slick and seawater.The results show that the EMD based feature extraction approach can identify oil slick accurately and effectively.
he Changbai Mountain was regard as experimental area in this text,based on the hyperspectral remote sensing data of CHRIS/PROBA 0 degrees was selected and preprocessed,several classification means of hyperspectral remote sensing to forest types classification of image were used ,such as maximum likelihood method (MLC)、minimum distance method、Support vector machine (SVM) method and Spectral Angle mapping method(SAM) etc.Finally,the real reference sources were used to verify the classification results.The results showed that SVM got the highest accuracy of 84.60% among all the forest type classification methods,the accuracies followed were MLC (83.53%),minimum distance method (73.81%) and SAM (56.49%).The Kappa coefficients were displayed from high to low: SVM (0.78),MLC (0.77),minimum distance method (0.68) and SAM (0.52).After the comparison of classification results,SVM obtained the highest accuracy in all classification methods.It showed the practicability and advantage of SVM applied to forest classification.
Using the ETM remote sensing image and two kinds of methods—Soble arithmetic which is one of the edge detection methods and MNDWI,there is going to research the coastline extraction in Yellow River delta which has artificial coastline and muddy coastline,then validate and evaluate the coastline extractions.The result indicates that the Soble arithmetic is more accurate.This is especially obvious in the muddy coastline,and we can find that the relative error has reduced to 0.018% from 0.025%.
The extraction of cultivated area has great practical and research significance.In this paper,a method of the scale transformation to extract farmland by using different pixel size satellite remote images is presented.Take the northern of Jiangsu plain for example,the proportion coefficient between the cultivated area in three scales,which is MODIS(250 m) image,CCD(19.5 m)and HR (2.36 m) of CBERS-02B,that is computed by sampling statistics method to increase the area accuracy.The coefficient of sample areas in this paper between MODIS images and truly area is 41.7%.At last the farmland area of MODIS image is calculated in this method and compared with the normal result.The experimental result indicated that this method has the good operationality and raise the accuracy,that may carry on the large range cultivated area the extraction.
According to the Ackerman's MODIS cloud detection technology and bispectral IR method,cloud detection and cloud phase retrieval in Changchun were conducted using MODIS L1B data at 04:25 and 18:00 on October 1,2008 (UTM).The data were geometric corrected,calibrated and clipped by ENVI before being used.The results showed that the cloud detection and day cloud phase retrieval were effective,but the night cloud phase retrieval was not so effective owing to the low temperature in the night.
Aiming at the Weihe river in Shaanxi,a gray expanded GA—BP(Genetic Algorithm—Back Propagation) neural network model is established in this paper,which combines the BP neural network improved by GA with the gray theory.It is used to retrieve the four main pollution index (permanganate index),(chemical oxygen demand),(ammonia nitrogen), (dissolved oxygen) for Weihe river.Experimental results show that compared with the ordinary BP neural network model and the traditional multiple linear regression model,the improved artificial neural network has better prediction accuracy,and can be used to retrieve Weihe river quality parameters by remote sensing.
In order to solve Weihe River water quality retrieving by remote sensing,which is regression estimation problem characterized by non-linear and small sample,least squares support vector regression (LSSVR) is introduced in this paper.It transforms the quadratic programming problems into linear equations,which reduce the computational complexity greatly,increase the speed of computing,and meanwhile assure the accuracy.To overcome difficulties in selecting the parameters of the model,genetic algorithm (GA) is used to optimize the parameters of the model.The proposed methods are used to carry on experiments to the standard data sets,and retrieve four water quality parameters CODmn (potassium permanganate index),NH3-N(ammonia nitrogen),DO (dissolved oxygen),COD(chemical oxygen demand) for Weihe River.The results show that GA-LSSVR model can be used to solve complex regression problems and has better prediction performance.
According to the characteristics of the surface mining facilities in the monitoring area.The remote sensing images are processed in related steps,and then combined with GIS analysis.The results are very close to the actual mining exploitation situation.So we can draw a conclusion that the use of GIS and RS in the mineral resource monitoring is conductive for the country to master the real situation of mining exploitation and offers the decision\|making support for the integrated management of the mineral resource development.
Non-contact photo Modeler system is used to detect the surface error of inflatable parabolic antenna,least squares method is utilized to obtain the RMS of the parabola.The possible errors are analyzed during the surface RMS measurement,the main factors that influence the inflatable parabolic antenna performance are also discussed.〖JP〗
The new design of improved ground-based atmospheric microwave sounder was presented.It has 7 channels in v-band (50~59 GHz) to retrieve temperature profiles and 7 channels in k-band (22~31 GHz) to retrieve humidity profiles.Different from MP3000,it adopts independent reflectors and direct-detect type,so it has simple structure,high spatial resolution,and low maintenance cost and small size.According to the radiometric transfer equation and radiosonde datasets,the weighting functions and brightness temperatures have been derived.Using back-propagation neural network,it can retrieve atmospheric temperature profiles.The results show that the prototype of ground-based atmospheric microwave sounder satisfies the requirements of spatial and temporal
According to the characteristic of SAR’s backscatter and imagery feature,the paper presents a distribution divergence method calculating the cross-entropy of Edgeworth polynomial to detect the change area in different temporal SAR images.The method simulates the SAR image’s probability distribution function with Edgeworth polynomial,and simulates the differentia index between two images with cross-entropy.The differentia image from differentia index reflects the images change,and reduces the influence of SAR images speckle noise.The detected results which is the different image segmentation results with CFAR demonstrate the method be of high detecting rate and low fault alarm rate.
Multibaseline SAR tomography and Polarimetric coherence tomography (PCT)are two extensions of interferometric SAR(InSAR).In the paper the basic concepts of multibaseline SAR tomography and PCT are illustrated.The current status of SAR tomography technique,spaceborne SAR tomography implementation,and the applications of SAR tomography, are summarized.
The oxygen A\|band,located in the visible and near\|infrared spectrum,demonstrates different spectral characteristics with the changes of wavelength and resolution.During the last several decades,great efforts have been affording to the research of its application for remote sensing.In this paper,the current and potential applications of oxygen A\|band are reviewed.Furthermore,the noticeable achievements in different areas and the trends of development in the future are summarized.It is hoped that the attention of atmospheric remote sensing community and the development of these fields would be aroused in China.
Multiple-point simulation (MPS) is one of the applications of multiple-point statistics in geology.It can simulate the complex geometry\|structure by getting the conditional probability from training image through search-tree while the traditional geostatistics has little ability to capture the complex geometry-structure.When MPS is applied into remote sensing classification,it can help us to improve the traditional-pixel-by-pixel-classification accuracy by combining the structural and spectral information.This article firstly introduces the development of new classification method which is called “CCSSM”,and then discusses the revision of this methods.Furthermore,it extends CCSSM’s application to the classification of the multiple-class from in the perspects of a theoretical and feasible way.
The signal-to-noise ratio (SNR) is one of the most important indices which can be used to evaluate the data quality obtained by a remote sensor.To a great extent,the SNR of an image reflects the SNR of the remote sensor.Several typical methods to estimate the SNR of optical remote sensing imagery are summarized in this paper,and their merit and restrictions are presented.And this paper also performs the comparison and analysis between these methods based on their own principles,from six aspects including the automatic computation,the computing time,the stability,the applicability,the suitable sensor category,and the uniformity of estimating areas.In addition,the paper points out that the comparison and analysis between methods in various specific applications should be done in the future.The study will help to choose a reasonable SNR estimating method aiming at different remote sensors and different types of remote sensing images.
