Multi-temporal high-resolution land cover (LC) information is of great significance to landscape monitoring, environmental assessment, and local climate change. Given the LC map in the former phase, an automatic LC updating approach based on change detection of high-resolution remote sensing images is proposed. First, object-based change detection is implemented combining spectral bands, normalized difference vegetation index, and normalized difference water index. Second, the changed objects are classified using training samples generated from the unchanged area, and the LC labels of the training samples were transferred from the LC map in the former phase. Finally, as the updated objects with abnormal area (AREA) or perimeter-area ratio (PARA) are recognized as slivers or spurious stretches, and removed using specifically designed rules, an AREA-PARA-based updating method is proposed to update the LC map. Two pairs of GaoFen-1 panchromatic and multispectral sensor images acquired in 2013 and 2015 of two areas in Jiangsu, China, were used to validate the effectiveness of the proposed method. Results and the comparisons with two other updating methods manifested the superiority of the APU method in reducing abnormal LC fragmentation and shape complexity, and maintaining LC consistency between two phases.
The unmanned aerial vehicle (UAV) plays an increasingly important role in monitoring and managing islands recently for their high feasibility and the miniaturization of sensors, which provide new possibilities for accurate island green cover mapping. We developed a framework that integrates UAV-acquired high-spatial resolution multispectral image and LiDAR data for effective object-based green cover mapping of Donkey Island in the Yellow Sea, China. LiDAR-derived structural and intensity information were combined with multispectral-derived spectral information for obtaining green cover objects. Five kinds of feature types [i.e., spectral, texture, height, intensity, and geometry features (GFs)] were calculated based on each object for green cover classification. Meanwhile, a multiple classifier system was adopted to improve the classification accuracy. The results indicate that the accuracy of green cover mapping could be significantly improved by the combination of multiple feature types. The inclusion of height and intensity features (IFs) can increase the overall classification accuracy by 7% and 5%, respectively, but the statistical significant differences are not found between these two feature types. The best green cover map is generated via a feature group obtained by the sequential backward selection with random forest method, reaching an overall accuracy of 88.5% and overall disagreement of 18.5%. Among the three major green cover classes, the accuracy of shrub class mapping improves the most when compared to classification using individual data, followed by tree and grass. Analysis of feature importance implies that spectral, height, and IFs are more beneficial to green cover mapping compared to texture and GFs. Furthermore, integrating multispectral and LiDAR data can provide more reliable green cover distribution maps and reduce the classification uncertainties.
Edge extraction from high spatial resolution (HSR) remotely sensed images is one of the essential tasks for image segmentation and object identification. We present an optimal Gabor-based edge detection method which mainly focuses on selecting optimal parameters, including central frequency and spectrum scale, for Gabor filter. The central frequency is automatically optimized by phase randomization and the human visual system-based structure similarity index. Next, the optimal spectrum scale is determined based on two-dimensional power spectrum density. The edge detection method is comprehensively discussed in the analysis of parameter sensitivity, overall performance, and comparative tests with several widely used methods. Qualitative and quantitative experimental studies, performed on six test images with various spatial resolution, show that the proposed method provides a promising solution to edge detection from HSR remotely sensed images.
Snow cover extraction in mountain areas is a complex task, especially from high spatial resolution remote sensing (HSRRS) data. The influence of mountain shadows in HSRRS is severe and normalized difference snow index-based snow cover extraction methods are inaccessible. A decision tree building method for snow cover extraction (DTSE) integrated with an efficiency feature selection algorithm is proposed. The severe influence of terrain shadows is eliminated by extracting snow in sunlight and snow in shadow separately in different nodes. In the feature selection algorithm, deviation of fuzzy grade matrix is proposed as a class-specific criterion which improves the efficiency and robustness of the selected feature set, thus making the snow cover extraction accurate. Two experiments are carried out based on ZY-3 image of two regions (regions A and B) located in Tianshan Mountains, China. The experiment on region A achieves an adequate accuracy demonstrating the robustness of the DTSE building method. The experiment on region B shows that a general DTSE model achieves an unsatisfied accuracy for snow in shadow and DTSE rebuilding evidently improves the performance, thus providing an accurate and fast way to extract snow cover in mountain areas.
In this paper a method of Fourier spectrum features based edge detection of urban street trees is described. The QuickBird image was first transformed by 2-D discrete Fourier transform. Then the energy of the component in spatial frequency was calculated. The energy distribution of the angle in max energy was used for further study. Different frequency segments was analyzed, the frequency that can best describe the street tree edge was chosen as the cut-off frequency of the street trees edge. Odd Gabor filter in frequency domain with the cut-off frequency and the max-energy angle was applied for the edge detection. The road center line is extracted by a Gabor filter in frequency domain. Then the edge of the street trees is restricted by the road center line. The edge detection result is analyzed by Canny criteria, and the ΣV=1.00, and C=0.89.
Because the resolution of remotely sensed imagery becomes higher, new methods are introduced to process the
high-resolution remotely sensed imagery. The algorithms introduced in this paper to recognize and extract the river
features based on the frequency domain. This paper uses the Gabor filter in frequency domain to enhance the texture of
river and remove the noise from remotely sensed imagery. And then according to the theory of phase congruency, this
paper retrieves the PC of every point such that features such as edge of river, building and farmland in the remotely
sensed imagery. Lastly, the skeletal methodology is introduced to determine the edge of river within the help of the trend
of river.
Segmentation has already been recognized as a valuable and complementary approach that performs a region-based
rather than a point-based evaluation of high-resolution remotely sensed data. An approach to segmentation of
multispectral IKONOS image based on texture marker-controlled watershed transform is presented. 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 to detect invariant edge features. Then a method for
multispectral IKONOS image segmentation based on band feature combination is demonstrated. After that an algorithm
to combining texture with edge features is presented and used to implement the marker-controlled watershed
segmentation. Finally empirical discrepancy is calculated to evaluate the segmentation results. It shows that the precision
of right segmentation rate is up to 75% to 85%.
Normally, agricultural land parcels being close to town centers and connected with road systems have higher probability
to be converted for newly planned settlements because these land parcels have higher accessibility influenced mainly by
the transportation condition. A comprehensive method is proposed to simulate the process of allocating land parcels for
the newly planned settlements with the raster data model using some elementary spatial analysis functions provided by
ArcGIS. The first step of the method is to construct the distribution of the cost-distance from each transferable land
parcel to the centers of administrative units at village level (called 'origin' for short). The second step is to simulate the
process to allocate the transferable land parcels for satisfying the land demand resulting from the development at these
'origins'. It is quite possible that more than one origin is interested in the same transferable land parcel. Therefore, how
to determine the priority of different origins to get the land parcel focused by more than one origin is emphatically
discussed as one key point of the proposed method in this paper. In addition, some other aspects, such as the size of the
transferable land parcels, which influences strongly on the results of assignment, are discussed as well. The proposed
method has been applied in the Sino-Germany project 'Sustainable Development by Integrated Land Use Planning' and
can meet the requirements.
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.
Electromagnetic radiance acquired by sensors is distorted mainly by atmospheric absorbing and scattering. Atmospheric
correction is required for quantitatively analysis of remote sensing information. Radiation transfer model based
atmospheric correction usually needs some atmospheric parameters to be chosen and estimated reasonably in advance
when atmospheric observation data is lacked. In our work, a radiometric calibration was applied on the satellite data
using revised coefficients at first. Then several parameters were determined for the correction process, taking into
account the earth's surface and atmospheric properties of the study area. Moreover, the atmospheric correction was
implemented using 6S code and the surface reflectance was retrieved. Lastly, the influence of atmospheric correction on
spectral response characteristics of different land covers was discussed in respects of the spectral response curve, NDVI
and the classification process, respectively. The results showed that the reflectance of all land covers decreases evidently
in three visible bands, but increases in the near-infrared and shortwave infrared bands after atmospheric correction.
NDVI of land covers also increases obviously after atmospheric influence was removed, and NDVI derived from the
surface reflectance is the highest comparing to that from the original digital number and the top of atmosphere
reflectance. The accuracy of the supervised classification is improved greatly, which is up to 87.23%, after the
atmospheric effect is corrected. Methods of the parameter determination can be used for reference in similar studies.
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.
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.
KEYWORDS: Agriculture, Stochastic processes, Data modeling, Roads, Remote sensing, Quantitative analysis, Analytical research, Fringe analysis, Process modeling, Geographic information systems
This paper analyzes and simulates the land use changes in the Pearl River Delta, China, using Longgang City as a case study. The region has pioneered the nation in economic development and urbanization process. Tremendous land use changes have been witnessed since the economic reform in 1978. Land use changes are analyzed and simulated by using stochastic cellular automata model, land use trajectories analysis, spatial indices and multi-temporal TM images of Longgang City (TM1987, TM1991, TM1995, TM1999, TM2003, TM2005) in order to understand how urbanization has transformed the non-urban land to urban land and estimate the consequent environment and ecological impacts in this region. The analysis and simulation results show that urban land continues to sprawl along road and fringe of towns, and concomitant to this development is the loss of agricultural land, orchards and fish ponds. This study provides new evidence with spatial details about the uneven land development in the Pearl River Delta.
KEYWORDS: Data modeling, Databases, Composites, Inspection, Geographic information systems, Process modeling, Roads, Systems modeling, Computer programming, Geoinformatics
A framework to express the changes and the causalities within the object's evolution is proposed based on a general inspection of the interactions among the Spatiotemporal Objects (STOs). Why the STOs can evolve is that they can exchange the material, the energy and the information mutually. The result of their evolution is the changes of their features and mechanism. Feature changes can be categorized into 2 levels: the changes of the feature statuses and the changes of the feature structures. The former result in just the increment of the data quantity of the database, the later can furthermore result in the increment of data structures defined in the database. Any a change of a STO is caused directly by a behavior of either itself or another STO. Feature changes can be directly expressed by spatiotemporal data or data structure, but the mechanism changes of a STO can only be indirectly reflected by its feature and behavior description. The proposed framework consists of five key elements: the essence, the features, and the behaviors of the STO, the information flow and the material flow within the spatiotemporal interactive process.
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.
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