Effective and dynamic recognition of winter wheat has important implications for the development of agriculture in In this paper, we proposed a method for winter wheat identification using particle swarm optimization-support vector machine (PSO-SVM) model and multi-temporal Sentinel-2A image. The eigenvector combination based on spectral information and the eigenvector combination based on texture information were constructed by using different phenological periods of winter wheat. The winter wheat was identified and extracted by PSO-SVM. The extraction accuracy under different feature band combinations was compared and analyzed. The results showed that PSO-SVM had higher accuracy than traditional SVM. Using PSO-SVM, the optimal combination was multi-temporal spectral and mean texture information combination and its classification accuracy was 91.25%. This paper provides a theoretical basis for the future use of Sentinel-2A data to extract other crop information.
Leaf area index (LAI) as an important vegetation biophysical parameter, is one of input parameters in the land surface model, which has a very close relationship with vegetation photosynthesis, evapotranspiration, precipitation and carbon flux exchange process. Crop LAI is a vital agronomic parameter, which can reflect the status of crop growth and predict crop yield. Accurate crop LAI has an important role in crop identification, growth monitoring, and food production estimation. This paper used the China Environmental monitoring Satellite (HJ-1) CCD data to estimate summer corn LAI based on PROSAIL radiative transfer model in Yucheng county, Shandong province, China. The mechanism and impact of leaf and canopy parameters on the canopy reflectance in different bands of HJ-1 CCD data were analyzed quantitatively using the simulated data. The results showed that the LAI estimates had relatively high accuracy with R=0.73, RMSE=0.32, and the PROSAIL model could be used to estimate LAI effectively from the perspective of radiative transfer principle. It can provide reference data for corn LAI estimation and growth monitoring in the study area.
Nanjing, a typical megacity in eastern China, has undergone dramatic expansion during the past decade. The surface urban heat island (SUHI) effect is an important indicator of the environmental consequences of urbanization and has rapidly changed the dynamics of Nanjing. Accurate measurements of the effects and changes resulting from the SUHI effect may provide useful information for urban planning. Index, centroid transfer, and correlation analyses were conducted to measure the dynamics of the SUHI and elucidate the relationship between the SUHI and urban expansion in Nanjing over the past decade. Overall, the results indicated that (1) the region affected by the SUHI effect gradually expanded southward and eastward from 2000 to 2012; (2) the centroid of the SUHI moved gradually southeastward and then southward and southwestward, which is consistent with the movement of the urban centroid; (3) the trajectory of the level-3 SUHI centroid did not correspond with the urban mass or SUHI centroids during the study period and (4) the SUHI intensity and urban fractal characteristics were negatively correlated. In addition, we presented insights regarding the minimization of the SUHI effect in cities such as Nanjing, China.
The methods of segment-based image analysis are becoming more and more important for remote sensing as a result of
the progresses in spatial resolution of satellite image. An approach to segmentation of IKONOS panchromatic image
based on frequency domain filtering and marker-controlled watershed transform is presented in the paper. Primarily the
texture and edge features are extracted from the response of log Gabor filtering. The texture features are obtained from
the amplitude response, and phase congruency is introduced as a new method to detect invariant edge features. Then an
approach to combining texture with edge features is presented and used to implement the marker-controlled watershed
segmentation. Combination of different frequency texture features is used to mark different complicated images. Finally
empirical discrepancy is calculated to evaluate the segmentation results. It shows that the precision of right segmentation
is up to 80~85%. The approach presented in the paper basically satisfies the demand of feature recognition and extraction
of high-resolution remotely sensed imagery.
KEYWORDS: Data modeling, Geographic information systems, Databases, Geoinformatics, Lead, Data storage, Associative arrays, Computing systems, Remote sensing, Information science
Driving by a happened event, entities vary from one state to another. Based on the rule, this paper analyzed the relations between events of entities and its states, and made an improvement on base state with amendments model. The improved model is named as multi base state with amendments model. The key idea of this method is to build more than one historical base state according to the frequency of event happens and the amount of data updates. And for the state between every historical base state, we merely stored the changed part but did not re-store the unchanged part. It overcomes the weakness of snapshot method which leads a great deal of redundant data, and also overcomes the drawback of base state with amendments method which will need a great amount of complex computation when historical state is rebuild. This model has been successfully applied to organize the spatio-temporal data of GIS in campus real estate information system. It is very convenient to rebuild house historical state.
There is important significance for hydrophytes extraction. It is the basis of water pollution control decision. For the purpose of hydrophytes extraction, the vegetation is classified into two species: submersed vegetation and emerged vegetation. And to obtain a better categorization map, three different classification methods as ISODATA, MLC and Decision tree are put forward in the paper. The analysis is performed on the Landsat TM image of Taihu lake acquired in 7, 2002. The result shows that the decision tree classification acquires the best extraction effect.
The LOD technology has an impact upon the multi-scale representation of spatial database. This paper takes advantage of LOD technology to express the multi-scale geographical data, and establish the exchange of multi-scale electronic map, further attain the goal that the details of geographic features such as point, line and polygon can be displayed more and more clearly with the display scale being enlarged to be convenient for the personnel of all offices of industry and commerce administration to label the locations of the corporations or enterprises.
With the recent availability of commercial high resolution remote sensing multispectral imagery from sensors such as
IKONOS and QuickBird, we can't get the accuracy of land-cover classification expected using pixel-based approach. In
this paper, we bring about object-based approach combined with the nearest neighbor to classify the QuickBird image of
LianYungang city. Firstly, the image is segmented into object feature, we make the shape feature and contextual relation
feature join the new feature space which is used to classify. And then we compare the classification of object-based
approach accuracy with the nearest neighbor method of classification result, we can draw a conclusion that the method of
classification in this paper can recognize geo-types much better. And the overall accuracy is 92.19%; the coefficient of
Kappa is 0.8835. Salt and pepper effect is decreased effectively. The result indicates that the approach of land-cover
classification combined object features with the nearest neighbor approach supplies another new technique for interpreting
high resolution remote sensed imagery.
During the last decades, researchers have mainly focused on improving of the pixel-based classification methods and their applications. As the image resolution improved, it can't get good classification result. In order to overcome this problem, the object-oriented approaches are introduced. In this paper, two methods were compared on urban area. A part of Nanjing city in china was selected as study area; TM and IKONOS imagery were employed. Three pixel-based classification methods, the maximum likelihood, ISODATA (Iterative Self-Organizing Data Analysis Technique), minimum distance method, and an object-oriented technique, the nearest neighbor method, were used to classify image, and evaluate the result. For TM imagery, the accuracy assessment of the results showed that the object-oriented classification approach couldn't get better classification result comparing to the pixel-based classification method, the salt-pepper phenomena of the pixel-based classification result images were not obvious. For IKONOS imagery, classification results provided by the object-oriented classification method were better than the pixel-based classification approaches. So, for urban classification using TM imagery, the traditional classification method could be used to get classification information and an acceptable result could be acquired. But when the IKONOS imagery was used to investigate the urban class, the object-oriented method could find the expected result.
The east Taihu lake region is characterized by high-density and large areas of enclosure culture area which tend to cause
eutrophication of the lake and worsen the quality of its water. This paper takes an area (380×380) of the east Taihu Lake
from image as an example and discusses the extraction method of combing texture feature of high resolution image with
spectrum information. Firstly, we choose the best combination bands of 1, 3, 4 according to the principles of the
maximal entropy combination and OIF index. After applying algorithm of different bands and principal component
analysis (PCA) transformation, we realize dimensional reduction and data compression. Subsequently, textures of the
first principal component image are analyzed using Gray Level Co-occurrence Matrices (GLCM) getting statistic Eigen
values of contrast, entropy and mean. The mean Eigen value is fixed as an optimal index and a appropriate conditional
thresholds of extraction are determined. Finally, decision trees are established realizing the extraction of enclosure
culture area. Combining the spectrum information with the spatial texture feature, we obtain a satisfied extracted result
and provide a technical reference for a wide-spread survey of the enclosure culture area.
When multispectral images are used to extract the area of aquatic vegetation in Taihu Lake, because of the influence of
suspended matter and algae, different objects may have the same spectrum and make it difficult to mapping the
distribution of aquatic vegetation exactly. Many different methods, including unsupervised classification and supervised
classification, are used, but the classification accuracy didn't improve obviously. The growth of aquatic vegetation is
closely to the water depth. So we try to use water depth data to increase the extraction accuracy. The whole Taihu Lake is
classified into three types: open water, emerged vegetation and submersed aquatic vegetation. Suppose the DN (Digital
number) of each type satisfies normal distribution. Numbers of sample points of each type in single band or combined
bands are selected and put down there DNs, and then statistical method is adopted to acquire the maximum and
minimum which are used to build decision tree to fulfill the classification. The single band or combined bands in which
maximum and minimum interval of each type have small intersect set are considered as the suitable bands for
classification. Two methods, classification based on spectral characteristics and classification based on spectral
characteristics and water depth data, are used. The classification accuracies of the two methods are compared. The results
show the water depth data can improve the classification accuracy and resolve the different objects with same spectrum
problem partially.
Phase Congruency is introduced as a frequency-domain based method to detect features from high-resolution remotely sensed imagery. Three types of objects were selected from the IKONOS pan imagery in Nanjing, i.e. paddy, road, and workshop objects. The Phase Congruency feature images were obtained by applying Phase Congruency model to these images with 2 octave log Gabor wavelets filters over 5 scales and 6 orientations. The outputs of space-domain based detectors Sobel and Canny are also presented for comparing to Phase Congruency. It is then shown the results that the magnitude of Phase Congruency response is largely independent of image local illumination and contrast, and Phase Congruency marks the line with a single response, not two. It is followed by a set of results illustrating the effects of varying filter parameters and noise in the calculation of Phase Congruency. It is found that Phase Congruency can obtain more accurate localization than space-domain based detectors because it does not need low-pass filtering to restrain noise first. The results also show that the noise has been successfully ignored in the smooth regions of the image, unlike the Canny detector results fluctuate all over the image.
Remote sensing detection model of damaged forest by tomicus piniperda was studied. It analyzed different detection models using multiple types of remote sensing data, such as TM, CBERS-1, AVHRR and MODIS data. The spectral features of the above remote sensing data (March, 2001) were given. And two detection models were put forward according to the spectral changing characteristics. One was named Difference Rate (DR) model with NIR and VIR data, which applied for TM, CBERS-1, AVHRR and MODIS. If DR was bigger, the forest grew healthier. Based on the typical sample, the different guidelines distinguished healthy and damaged forests were obtained. The other model was named Disaster Index (DI) model with thermal and NIR data, only suitable for MODIS. The guidelines of healthy and damaged forest were determined too. Greater DI was, the forest was stricken more badly. In conclusion, it will help monitor and assess the vermin occurrence and impact by remote sensing detection model.
This paper analyzed the damaged forest by tomicus piniperda using multiple types of remote sensing data such as TM, CBERS-1, AVHRR and MODIS data. It selected a typical region including heavy damaged and healthy forest. The region was located by GPS (Global Position System). Then the spectral features of the above remote sensing data (March, 2001) were given. It indicates that the values of healthy forest of TM NIR band (0.76-0.9 ) and SWIR band (1.55-1.75 ) are distinctly greater than those of damaged forest. The values of CBERS-1 NIR bands (0.77-0.89 ), AVHRR bands (0.725-1.0 ) and MODIS bands (0.841-0.876 ) behave in the same pattern with TM. Otherwise, the values of MODIS thermal bands (3.929-3.89 , 10.78-11.28 and 11.77-12.27 ) of damaged forest are distinctly greater than those of healthy forest. The AVHRR thermal bands are not so. Finally, two detection models were put forward according to the spectral changing characteristics. One was named Difference Rate (DR) model with NIR and VIR data, which applied for TM, CBERS-1, AVHRR and MODIS. DR is greater, the forest grow healthily. Basis on the typical sample, the different guidelines distinguished healthy and damaged forests are obtained. The other model was named Disaster Index (DI) model with thermal and NIR data, only suitable for MODIS. The guidelines of healthy and damaged forest are determined too. DI is greater the forest is stricken more badly. In conclusion, it will help monitoring and assessing the vermin occurrence and impact by remote sensing detection model.
In the paper, experiments and analysis of three pixel-based fusion methods had been discussed. The fusion methods include IHS, PCA and Brovey transform method. The fusion experiments were carried out in two circs, that is, between Landsat TM multi-spectral data and SPOT-4 Pan data, Landsat TM multi-spectral data and IRS-C Pan data. From the fusion results, the definition of all fusion images were improved greatly compared to the Landsat TM image. Especially the linear ground objects are much clear, such as the roads, the residents, the bridges, etc. According to the fusion between Landsat TM data and SPOT-4 Pan data, the Brovey fusion method was the best one. The PCA fusion method was better than the IHS fusion method. According to the fusion between Landsat TM data and IRS-C Pan data, the Brovey fusion method was also the best one. But the IHS fusion method was better than the PCA fusion method. Maximum likelihood method of classification was carried out on the fusion result, and classification accuracy of the classification results were evaluated. From the evaluation result, it can be concluded that classification accuracy of the Brovey fusion result is the highest between Landsat TM data and IRS-C Pan data. Classification accuracy of the IHS fusion result is the highest between Landsat TM data and SPOT-4 Pan data.
Support vector machine (SVM) is a newly learning machine. In the paper, it applied the SVM method to research on remote sensing multi-spectral classification using Landsat TM data. It selected the typical low-hill area as study site, which was located on the southern of the Yangze River, China. The land cover types were divided into six categories, which were the waterbody, the construction land, the paddy field, the woodland, the teagarden, and the bare land, etc. The classification of the study site using the Kohonen networks method was also given. The classification results show that classification accuracy of the SVM method is better than that of the Kohonen Networks method. Especially it could discriminate the woodlands from the mountainous shadow. In conclusion, the SVM method could gain higher classification accuracy using smaller training sample in low-hill area. It could also solve the confusion problems among the ground objects.
Geometric and radiometric correction, image processing, information extraction and the integration of remote sensing, GIS and GPS in the specific approach for dynamic monitoring of land resources in mountainous areas are discussed. A synthesized method combining the image difference approach with comparison post classification is employed and a monitoring system based on remote sensing, GIS and GPS are set up. Different illumination conditions are key factors influencing the spectral features in mountainous areas, thus the comprehensive analysis of DEM and NDVI are employed to restrain the influence of terrain. Errors also commonly generate in the registration of different temporal images and much change information is usually lost when the mean-value smoothing template is employed in the image processing in mountainous areas. To reduce the information lost, a regional auto-adaptive smoothing template is employed. As a case study, according to the specific characteristics of mountainous areas, the TM images acquired from both 1994 and 1996 are processed for land change detection in Renhe District, Sichuan. Field experiments for radiometric correction are conducted in the areas of 25 Km2 in this district. The changed areas are precisely surveyed and validated after the fieldwork in which the database of detailed land survey is acquired. Combined with Geological Information System (GIS) technology and Global Position System (GPS), a 3S-based dynamic monitoring system of land resources change information in Renhe District is established, which helps the data renewal and daily management. Finally, the key factors influencing the accuracy of information extracting in mountainous areas are discussed.
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