ISPRS Journal of Photogrammetry and Remote Sensing, 2011
This paper presents a novel approach to building roof modeling, including roof plane segmentation... more This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log 2 n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.
IEEE Transactions on Geoscience and Remote Sensing, 2010
This paper presents a solution framework for the segmentation and reconstruction of polyhedral bu... more This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated.
International Journal of Geographical Information Science, 2008
This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic k... more This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighborhood development level, which is taken by cellular automata as initial approximation in its transition rules. Such approximations are updated through spatial calibration on a township gird and temporal calibration with multi temporal satellite images. Assessment of the modeling results is based on three evaluation measures: Fitness, Type I and Type II errors. The approach is applied to model the growth of city Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100% 20% with 30% average errors can be achieved for 80% of the townships in urban growth prediction.
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
This paper presents a global solution to building roof topological reconstruction from LiDAR poin... more This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global....
Remotely sensed images are invaluable to acquire geospatial information about earth surface for t... more Remotely sensed images are invaluable to acquire geospatial information about earth surface for the assessment of land resources and environment monitoring. In most cases, the information provided by a single sensor is not complete or sufficient. Therefore, images collected by different sensors are combined to obtain complementary information.
This paper presents a methodology for simulating urban growth phenomenon through utilizing remote... more This paper presents a methodology for simulating urban growth phenomenon through utilizing remote sensing imagery and neural network (NN) algorithms. Historical satellite images of Indianapolis city, IN were used. All images were rectified and registered to Universal Transverse Mercator (UTM) NAD83 zone 16N. Supervised classification was used to classify the images to different land use categories. Seven classes were identified: water, road, residential, commercial, forest, pasture grasses and row crops. Image fusion was tested to examine its effect on the classification results. Overall, the classification accuracy using original images was better than the results from the fused ones. To implement NN algorithms to simulate the urban growth; focus was directed to the residential and commercial classes and their growth. The boundaries of these areas were extracted for each of the growth years. Radial extent of the boundary at specified angles was measured using different city centres. Radial distances and growth years were used as inputs to train the neural network algorithms. Two NN algorithms were used to simulate the urban growth: simple linear NN and back propagation (BP). Each algorithm was trained using the available data as well as interpolated data produced through NN function approximation algorithm. Short and long term urban boundary predictions were performed. Results showed that both algorithms after increasing the volume of the dataset succeeded in simulating the growth trends with better results achieved using the simple linear NN. Visual check and similarity of simulated and real growths were tested.
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper presents a novel approach to LiDAR filtering of ALS (Airborne Laser Scanning) data. Th... more This paper presents a novel approach to LiDAR filtering of ALS (Airborne Laser Scanning) data. The main effort is devoted to simplify and overcome the shortcomings of the existing morphological filtering algorithms. The proposed approach is based on the morphological erosion operation. The filtering is applied only to the points of discontinuity, which are identified from their residuals. The threshold for identifying the points of discontinuity is adaptively determined during the iteration. Our experiments show the proposed approach produces satisfying results in different terrain conditions with minimal change of parameters.
Natural disasters of any kind play havoc with and cause huge losses to both humans and properties... more Natural disasters of any kind play havoc with and cause huge losses to both humans and properties. Recent flooding in Pakistan is one of the true examples of how floods of such a magnitude can put an entire country in chaos and adversely affect its economy. These floods affected all the provinces of the country badly. Recent floods are the result of heavy and continuous spells of monsoon rains in the last week of July to the mid of August in most of the areas of the country, especially the northern areas. In most of the affected areas, an average of about 11 inches of daily rainfall was recorded for three days consecutively. These rains caused heavy flooding in the Indus, Swat and Kabul Rivers, and these remained at very high to extremely high flood/danger levels. As the results of these floods, over a thousand of people lost their lives, thousands of houses are damaged, a number of small villages and towns submerged, and most of the crops are destroyed. Kilometers of road segments ...
development, airborne laser ranging (lidar) technology is becoming popular and well accepted in t... more development, airborne laser ranging (lidar) technology is becoming popular and well accepted in the past few years for rapid, direct 3D urban landscape model generation at a resolution of up to a few centimeters.
2008 IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008), 2008
This paper presents the initial results of the Algorithm Performance Contest that was organized a... more This paper presents the initial results of the Algorithm Performance Contest that was organized as part of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation.
... Ejaz Hussain is with the National University of Science and Technology, Islamabad, Pakistan, ... more ... Ejaz Hussain is with the National University of Science and Technology, Islamabad, Pakistan, and formerly with the School of Civil Engineering, Purdue University ... It is found that most of the damage is to the concrete and masonry structures in the well planned areas of the city ...
Traditional image matching approaches often fail in processing Mars Global Surveyor stereo images... more Traditional image matching approaches often fail in processing Mars Global Surveyor stereo images because of low contrast and insufficient number of features. Reliable and precision matching tool is needed for precise Mars digital elevation model generation. This paper presents a hierarchical image matching approach. First, a number of well identified points are manually measured in a stereo pair. These measurements are input into a commercial digital photogrammetric tool as 'seed points' to generate more corresponding points. After that, the Delaunay triangulation network is formed for those seed points. The initial correspondence of an interest point on the other image is located by using the bilinear polynomial transformation whose coefficients are determined by six points closest to the interest point in the triangle. The conjugate point of the interest point in each triangle of the network is determined by using the parameters of the polynomial equation. In the next iteration, all the above generated corresponding points and the original seed points will be triangulated and the network will be densified in the same manner. This process repeats until the required point density is achieved. This initial prediction is then refined by using cross correlation and the least squares matching approaches. Based on this approach, we have matched stereo image pairs over selected landing sites for the Mars Exploration Rover mission. Assessment of image results suggests a matching consistency of better than one pixel. Presented in this paper are results of detailed matching quality evaluation and the generated digital elevation models.
A new algorithm, called RIMM (Reversed Iterative Mathematic Morphological) algorithm, is proposed... more A new algorithm, called RIMM (Reversed Iterative Mathematic Morphological) algorithm, is proposed for automatic derivation of building region information from LiDAR (Light Detection And Ranging) data. The main contribution of the proposed algorithm is to provide a reliable way for building point detection from airborne LiDAR data, in which the reversed iteration makes that the thresholds can be determined in a simple way, thus guaranteeing the applicability of this algorithm for various complicated real situations. An experimental region with 2.8 million LiDAR points, containing many buildings with various roof structures, various sizes, various orientations, and various roof texture conditions, are selected to validate the effect and applicability of this algorithm. The average values of commission and omission error in building information derivation are 5.7% and 8.7%, respectively. The experimental results indicate that this RIMM algorithm is able to derive building region information effectively.
Zonal analysis in geographic information systems is a useful and convenient tool to study the acc... more Zonal analysis in geographic information systems is a useful and convenient tool to study the accuracy of the digital elevation model ͑DEM͒ in terms of topographic complexity, which is defined in this paper as the change in terrain slope or slope change. The accuracy of the U.S. Geological Survey 1-degree DEM over two test areas is studied by comparing it with the USGS 7.5-min DEM. The statistical quantities of the DEM errors are studied and modeled using various mathematical functions. It is shown that the standard deviation of the 1-degree DEM can be largely approximated with a linear function of the slope change, while its minimum and maximum errors remain almost unchanged and occur in all slope change zones as a behavior independent of the terrain complexity.
Panchromatic and multispectral images are useful for the acquisition of geospatial information ab... more Panchromatic and multispectral images are useful for the acquisition of geospatial information about the Earth surface for the assessment of land resources and environment monitoring. Panchromatic images usually have a better spatial resolution than the multispectral ones of the same sensor, while the multispectral images provide spectral properties of the objects. Image fusion methods are needed to find the missing spatial details in the multispectral images using the panchromatic ones and transfer these details into the multispectral images without or with limited spectral content distortion. This study addresses two classes of image fusion approaches: colour-based methods and statistical methods. Specifically, the traditional RGB to IHS transform is generalized from 3-D to n-D such that it can handle multiple image bands. As for the statistical methods, we propose a criteria-based approach that produces fusion products to meet a set of predefined desired properties. Principles, s...
International Journal of Geographical Information Science, 2008
This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic k... more This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighborhood development level, which is taken by cellular automata as initial approximation in its transition rules. Such approximations are updated through spatial calibration on a township gird and temporal calibration with multi temporal satellite images. Assessment of the modeling results is based on three evaluation measures: Fitness, Type I and Type II errors. The approach is applied to model the growth of city Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100% 20% with 30% average errors can be achieved for 80% of the townships in urban growth prediction.
Mapping land cover in urban areas helps understanding the complexity of the urban landscape and e... more Mapping land cover in urban areas helps understanding the complexity of the urban landscape and environments. High resolution image data effectively captures such urban complexities and offers great potential for mapping the urban features in detail. The land cover information derived from remote sensing data has proven its usefulness for a wide range of urban applications. The traditional pixel-based classifiers rely on spectral information only, thus unable to capture the complexity and diversity of urban environments inherent in the high resolution image data. On the other hand, the processing at object level with additional spatial and contextual information produces promising mapping results. Urban land cover mapping from high resolution imagery with additional geospatial data using object-based fuzzy image classification techniques produced higher overall accuracy (90%). This paper explores the applicability and transferability of the fuzzy rule set developed over small areas to large areas with similar characteristics. The inheritance of rule set resulted into a slight decrease in overall classification accuracy (5%) that is due to certain new classes not represented previously. However, quickly updated rule set produced improved classification results and reduced processing time considerably.
ISPRS Journal of Photogrammetry and Remote Sensing, 2011
This paper presents a novel approach to building roof modeling, including roof plane segmentation... more This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log 2 n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.
IEEE Transactions on Geoscience and Remote Sensing, 2010
This paper presents a solution framework for the segmentation and reconstruction of polyhedral bu... more This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated.
International Journal of Geographical Information Science, 2008
This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic k... more This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighborhood development level, which is taken by cellular automata as initial approximation in its transition rules. Such approximations are updated through spatial calibration on a township gird and temporal calibration with multi temporal satellite images. Assessment of the modeling results is based on three evaluation measures: Fitness, Type I and Type II errors. The approach is applied to model the growth of city Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100% 20% with 30% average errors can be achieved for 80% of the townships in urban growth prediction.
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
This paper presents a global solution to building roof topological reconstruction from LiDAR poin... more This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global....
Remotely sensed images are invaluable to acquire geospatial information about earth surface for t... more Remotely sensed images are invaluable to acquire geospatial information about earth surface for the assessment of land resources and environment monitoring. In most cases, the information provided by a single sensor is not complete or sufficient. Therefore, images collected by different sensors are combined to obtain complementary information.
This paper presents a methodology for simulating urban growth phenomenon through utilizing remote... more This paper presents a methodology for simulating urban growth phenomenon through utilizing remote sensing imagery and neural network (NN) algorithms. Historical satellite images of Indianapolis city, IN were used. All images were rectified and registered to Universal Transverse Mercator (UTM) NAD83 zone 16N. Supervised classification was used to classify the images to different land use categories. Seven classes were identified: water, road, residential, commercial, forest, pasture grasses and row crops. Image fusion was tested to examine its effect on the classification results. Overall, the classification accuracy using original images was better than the results from the fused ones. To implement NN algorithms to simulate the urban growth; focus was directed to the residential and commercial classes and their growth. The boundaries of these areas were extracted for each of the growth years. Radial extent of the boundary at specified angles was measured using different city centres. Radial distances and growth years were used as inputs to train the neural network algorithms. Two NN algorithms were used to simulate the urban growth: simple linear NN and back propagation (BP). Each algorithm was trained using the available data as well as interpolated data produced through NN function approximation algorithm. Short and long term urban boundary predictions were performed. Results showed that both algorithms after increasing the volume of the dataset succeeded in simulating the growth trends with better results achieved using the simple linear NN. Visual check and similarity of simulated and real growths were tested.
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper presents a novel approach to LiDAR filtering of ALS (Airborne Laser Scanning) data. Th... more This paper presents a novel approach to LiDAR filtering of ALS (Airborne Laser Scanning) data. The main effort is devoted to simplify and overcome the shortcomings of the existing morphological filtering algorithms. The proposed approach is based on the morphological erosion operation. The filtering is applied only to the points of discontinuity, which are identified from their residuals. The threshold for identifying the points of discontinuity is adaptively determined during the iteration. Our experiments show the proposed approach produces satisfying results in different terrain conditions with minimal change of parameters.
Natural disasters of any kind play havoc with and cause huge losses to both humans and properties... more Natural disasters of any kind play havoc with and cause huge losses to both humans and properties. Recent flooding in Pakistan is one of the true examples of how floods of such a magnitude can put an entire country in chaos and adversely affect its economy. These floods affected all the provinces of the country badly. Recent floods are the result of heavy and continuous spells of monsoon rains in the last week of July to the mid of August in most of the areas of the country, especially the northern areas. In most of the affected areas, an average of about 11 inches of daily rainfall was recorded for three days consecutively. These rains caused heavy flooding in the Indus, Swat and Kabul Rivers, and these remained at very high to extremely high flood/danger levels. As the results of these floods, over a thousand of people lost their lives, thousands of houses are damaged, a number of small villages and towns submerged, and most of the crops are destroyed. Kilometers of road segments ...
development, airborne laser ranging (lidar) technology is becoming popular and well accepted in t... more development, airborne laser ranging (lidar) technology is becoming popular and well accepted in the past few years for rapid, direct 3D urban landscape model generation at a resolution of up to a few centimeters.
2008 IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008), 2008
This paper presents the initial results of the Algorithm Performance Contest that was organized a... more This paper presents the initial results of the Algorithm Performance Contest that was organized as part of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation.
... Ejaz Hussain is with the National University of Science and Technology, Islamabad, Pakistan, ... more ... Ejaz Hussain is with the National University of Science and Technology, Islamabad, Pakistan, and formerly with the School of Civil Engineering, Purdue University ... It is found that most of the damage is to the concrete and masonry structures in the well planned areas of the city ...
Traditional image matching approaches often fail in processing Mars Global Surveyor stereo images... more Traditional image matching approaches often fail in processing Mars Global Surveyor stereo images because of low contrast and insufficient number of features. Reliable and precision matching tool is needed for precise Mars digital elevation model generation. This paper presents a hierarchical image matching approach. First, a number of well identified points are manually measured in a stereo pair. These measurements are input into a commercial digital photogrammetric tool as 'seed points' to generate more corresponding points. After that, the Delaunay triangulation network is formed for those seed points. The initial correspondence of an interest point on the other image is located by using the bilinear polynomial transformation whose coefficients are determined by six points closest to the interest point in the triangle. The conjugate point of the interest point in each triangle of the network is determined by using the parameters of the polynomial equation. In the next iteration, all the above generated corresponding points and the original seed points will be triangulated and the network will be densified in the same manner. This process repeats until the required point density is achieved. This initial prediction is then refined by using cross correlation and the least squares matching approaches. Based on this approach, we have matched stereo image pairs over selected landing sites for the Mars Exploration Rover mission. Assessment of image results suggests a matching consistency of better than one pixel. Presented in this paper are results of detailed matching quality evaluation and the generated digital elevation models.
A new algorithm, called RIMM (Reversed Iterative Mathematic Morphological) algorithm, is proposed... more A new algorithm, called RIMM (Reversed Iterative Mathematic Morphological) algorithm, is proposed for automatic derivation of building region information from LiDAR (Light Detection And Ranging) data. The main contribution of the proposed algorithm is to provide a reliable way for building point detection from airborne LiDAR data, in which the reversed iteration makes that the thresholds can be determined in a simple way, thus guaranteeing the applicability of this algorithm for various complicated real situations. An experimental region with 2.8 million LiDAR points, containing many buildings with various roof structures, various sizes, various orientations, and various roof texture conditions, are selected to validate the effect and applicability of this algorithm. The average values of commission and omission error in building information derivation are 5.7% and 8.7%, respectively. The experimental results indicate that this RIMM algorithm is able to derive building region information effectively.
Zonal analysis in geographic information systems is a useful and convenient tool to study the acc... more Zonal analysis in geographic information systems is a useful and convenient tool to study the accuracy of the digital elevation model ͑DEM͒ in terms of topographic complexity, which is defined in this paper as the change in terrain slope or slope change. The accuracy of the U.S. Geological Survey 1-degree DEM over two test areas is studied by comparing it with the USGS 7.5-min DEM. The statistical quantities of the DEM errors are studied and modeled using various mathematical functions. It is shown that the standard deviation of the 1-degree DEM can be largely approximated with a linear function of the slope change, while its minimum and maximum errors remain almost unchanged and occur in all slope change zones as a behavior independent of the terrain complexity.
Panchromatic and multispectral images are useful for the acquisition of geospatial information ab... more Panchromatic and multispectral images are useful for the acquisition of geospatial information about the Earth surface for the assessment of land resources and environment monitoring. Panchromatic images usually have a better spatial resolution than the multispectral ones of the same sensor, while the multispectral images provide spectral properties of the objects. Image fusion methods are needed to find the missing spatial details in the multispectral images using the panchromatic ones and transfer these details into the multispectral images without or with limited spectral content distortion. This study addresses two classes of image fusion approaches: colour-based methods and statistical methods. Specifically, the traditional RGB to IHS transform is generalized from 3-D to n-D such that it can handle multiple image bands. As for the statistical methods, we propose a criteria-based approach that produces fusion products to meet a set of predefined desired properties. Principles, s...
International Journal of Geographical Information Science, 2008
This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic k... more This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighborhood development level, which is taken by cellular automata as initial approximation in its transition rules. Such approximations are updated through spatial calibration on a township gird and temporal calibration with multi temporal satellite images. Assessment of the modeling results is based on three evaluation measures: Fitness, Type I and Type II errors. The approach is applied to model the growth of city Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100% 20% with 30% average errors can be achieved for 80% of the townships in urban growth prediction.
Mapping land cover in urban areas helps understanding the complexity of the urban landscape and e... more Mapping land cover in urban areas helps understanding the complexity of the urban landscape and environments. High resolution image data effectively captures such urban complexities and offers great potential for mapping the urban features in detail. The land cover information derived from remote sensing data has proven its usefulness for a wide range of urban applications. The traditional pixel-based classifiers rely on spectral information only, thus unable to capture the complexity and diversity of urban environments inherent in the high resolution image data. On the other hand, the processing at object level with additional spatial and contextual information produces promising mapping results. Urban land cover mapping from high resolution imagery with additional geospatial data using object-based fuzzy image classification techniques produced higher overall accuracy (90%). This paper explores the applicability and transferability of the fuzzy rule set developed over small areas to large areas with similar characteristics. The inheritance of rule set resulted into a slight decrease in overall classification accuracy (5%) that is due to certain new classes not represented previously. However, quickly updated rule set produced improved classification results and reduced processing time considerably.
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