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The article offers information on the winners of the ESRI Award for Best Scientific Paper in GIS awards. It states that the American Society for Photogrammetry and Remote Sensing (ASPRS) ESRI Award Selection Committee made the announcement concerning the winners of this award. Winners include Rongxing Li, Jue Wang, and Sanchit Agarwal for their paper "A WebGIS for Spatial Data Processing, Analysis, and Distribution for the MER 2003 Mission," Qiuming Cheng and C. Vincent Tao for their paper "A Network-centric Modeling Approach for Infrastructure Interdependency," and Pravara Thanapura and Dennis L. Helder for their work "Mapping Urban Land Cover Using Quickbird NDVI and GIS Spatial Modeling for Runoff Coefficient Determination."

The article offers information on the winners of the Boeing Award for Best Paper in Image Analysis and Interpretation. The American Society for Photogrammetry and Remote Sensing (ASPRS) Boeing Award Selection Committee has announced that Xiaoliang Lu, Ronggao Liu, Jiyuan Liu, and Shunling Liang have received this award. Relative to this, it is posed that presentation of this award will take place in May 2008 during the ASPRS Annual Conference to be conducted in Portland, Oregon. The award aims to induce development and recognize achievement in image interpretation and analysis through acknowledgement of efficient publication in this field.

Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for lidar point cloud processing that takes advantage of the implicit topology of various lidar sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D lidar scenes acquired using street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects. Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

The use of disparity information is computationally cost-effective for 3D-assisted mapping applications. However, off-nadir satellite images over dense urban areas suffer from severe occlusion. Therefore, large gaps will be created due to the unsuccessful matching in the occluded areas. Commonly, gap interpolation produces misleading information that destroys the quality of the subsequent information extraction application. Hence, the more reliable solution is to fill the occlusion gaps by supplementary data. However, disparity maps are the co-relation of one stereo pair. Hence, supplementary disparity maps cannot be directly generated and applied. Thus, this paper introduces the Double Projection Plane (DPP) method for constructing disparity proportionality among multi-view stereo satellite images and calculating analytically the transferring scales required for producing supplementary disparity data. Accordingly, based on multi-view stereo satellite images, this method is promising to provide gap-and-outlier-free disparity maps that have the potential to replace the need for elevation models to some extent. [ABSTRACT FROM AUTHOR]

Local governments frequently use parcel maps or other spatial data for decision-making, but much of this data is often inaccurate and outdated, producing a negative influence on the expected outcome. Remote sensing can provide accurate and current high-resolution imagery. However, one major bottle-neck to the integration of high-resolution imagery into existing geographic information systems is the issue of positional accuracy of the existing line-work within the vector GIS data-base, making it difficult to match with imagery. This paper presents a vector-to-imagery conflation approach to improve the positional accuracy of digital parcel maps by conflating the vector parcel maps to make it consistent with high-resolution imagery. Road intersections are automatically extracted from imagery and used as control points. A relaxation labeling algorithm is used to find the matches between the two road intersection point sets from vector and imagery. The links are created from the matched point pairs and are used to perform a piecewise transformation. The test results show that this approach can improve the accuracy of vector parcel maps significantly. It is a very cost-effective method and has great potential to save considerable time and money for local governments to upgrade their inaccurate vector datasets. [ABSTRACT FROM AUTHOR]

Characterization of urban land uses is essential for many applications. However, differentiating among thematically-detailed urban land uses (residential, commercial, industrial, institutional, recreational, etc.) over broad areas is challenging, in part because image-based solutions are not ideal for establishing the contextual basis for identifying economic function and use. At present no current United States national-scale mapping exists for urban land uses similar to the classical Anderson Level II classification. This paper describes a product that maps urban land uses, and is linked to and corresponds with the National Land Cover Database (NLCD) 2006. In this product, NLCD urban pixels, in addition to their current imperviousness intensity classification, are assigned one of nine urban use classes based on information drawn from multiple data sources. These sources include detailed infrastructure information, population characteristics, and historical land use. The result is a method for creating a 30 m national-scale grid providing thematically-detailed urban land use information which complements the NLCD. Initial results for 10 major metropolitan areas are provided as an on-line link. Accuracy assessment of initial products yielded an overall accuracy of 81.6 percent. [ABSTRACT FROM AUTHOR]

Effective large-scale landslide mapping is becoming significantly important for analyzing natural hazards and providing landslide locations rapidly for emergency response. Change detection and machine learning methods are commonly used for landslide detection. Change detection mostly relies on several experienced parameters that users have to tune for different images, which limits the practical application. The training machine learning model consumes much time, and it is limited to specific imaging conditions. In this paper, a simple method for landslide detection using a fixed parameter by calculating image saliency is proposed. Landslide is detected as a saliency object within the background of vegetation and bare rocks. It is fast and robust for the experimental images, and outperforms the state-of-the-art, semi-automatic method in terms of accuracy and computing time. Given the high efficiency and robustness of the proposed method, it is applicable to practical cases for hazard estimation. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-texture change detection method by integrating macro- and micro-texture features. Macro-textures are related to the information depned by the whole image scene, while micro-textures describe distributions and relationships of the gray levels within a local window. Moreover, we propose two strategies, random forests (RF) and a fuzzy set model, to integrate different characteristics of the textures. Experiments were conducted on ZY-3 (the prst civilian highresolution stereo mapping satellite of China) orthographic images of the cities of Wuhan and Tokyo, as well as WorldView-2 multi-spectral images of the city of Kuala Lumpur. Results showed that the wavelet-based features obtained the highest accuracy among the macro-textures, while the morphological attributes obtained the best results for the micro-textures. Ry integrating both micro- and macro-textures, the texture combination using both RF and a fuzzy set model can further improve the accuracy of change detection. [ABSTRACT FROM AUTHOR]

Using publically available remotely sensed images to map wetlands and invasive plants is attractive to ecologists, environmental scientists, and managers. However, wetland and invasive plant mapping on the basis of no- or low-cost images has been challenged by the variability of mapping accuracy. In this paper, we are developing an innovative wetland and invasive plant mapping technique characterized with three integrations: the integration of image interpretation with feature extraction, the integration of high spatial-resolution images with high spectral-solution images, and the integration of field reference data with interpreted and classified images. This technique advocates standard procedures for integrating n a i p (National Agriculture Imagery Program) and Landsat images with multiple processes of ground truthing, image classification, and validation. The case study conducted in the Detroit River International Wildlife Refuge concludes that the integration of n a i p and Landsat images provides sufficient spatial and spectral information for mapping coastal wetlands and Phragmites. [ABSTRACT FROM AUTHOR]

Image classification remains dependent on user intervention for class label assignment. Whether that effort takes place in advance of or post classification is immaterial. This paper explores a novel approach to automating the assignment of class labels using a normalized spectral distance measure and a hyperspectral library. The technique resulted in an automatically labeled agricultural map with an overall classification accuracy of 51 percent, outperforming the manual labeling (40 percent to 45 percent accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39 percent), and was comparable to, or lower than, the classification accuracy of a Maximum Likelihood supervised technique (53 percent to 63 percent) depending on the analyst. The newly developed class-labeling algorithm provided better results for the majority of targets while having similar performance to manual labeling on targets that are particularly difficult to differentiate in a purely spectral manner. [ABSTRACT FROM AUTHOR]

Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of >96 percent (Khat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values >0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making. [ABSTRACT FROM AUTHOR]

Lidar has been established over the past few years as a mainstream tool for the acquisition of three dimensional point data. Besides the conventional mapping missions, lidar has proven to be very useful for a wide spectrum of applications such as forestry, urban planning, structural deformation analysis, and reverse engineering. In the context of a nationwide dataset, it is safe to assume that multiple laser scanners are under different conditions to collect the data. Current registration and segmentation algorithms assume homogeneity in the local point density and accuracy which is an invalid assumption that cannot be tolerated. As a consequence of the wide range of lidar sensors currently available, it is becoming crucial to develop algorithms for the registration and segmentation of lidar data with significantly varying characteristics (i.e., varying point density and accuracy). In this paper, a methodology for the optimal registration and segmentation of heterogeneous lidar data is presented. An example of integrating airborne and terrestrial laser scans is presented and followed by a discussion of the pros of the integration process. [ABSTRACT FROM AUTHOR]

Remote sensing has traditionally been used to retrieve water constituents by establishing a relationship between in situ measured quantities and image-derived products. Motivated by the dramatically improved potential of the Landsat Data Continuity Mission (LDCM), this paper describes a different approach for water constituent retrieval where both thermal and visible spectral bands of the Enhanced Thematic Mapper Plus (ETM+) instrument on board Landsat-7 are utilized. In this effort, Landsat data is integrated with a 3D hydrodynamic model to obtain profiles of particles and dissolved matter in the near shore zone in the vicinity of two river discharges. The procedure is based upon performing many hydrodynamic simulations by adjusting input environmental/physical variables and generating Look-Up- Tables (LUTS). The best match, obtained using optimization, demonstrated an average root-mean-squared-error (RMSE) of 0.68 percent, i.e., 0.0068 reflectance units, calculated over the two river plumes. It is concluded that calibrating a physics-based model using the Landsat-7 imagery can provide a more lucid insight into the dynamics of spatially non-uniform waters. [ABSTRACT FROM AUTHOR]

The Hellenic Cadastre Program (HCP) of Greece aims at developing a modern cadastral system for the first time in the Hellenic history. This paper is focused on the issues related to cadastral forestlands digital mapping, an indispensable part of HCP. Mapping the forestlands is a challenge of multiple disciplines. It includes photogrammetry, photointerpretation, Geographic Information Systems (GIS), and a clear understanding of the current institutional and legislative setting. The process requires both historical and current information pertaining to land-cover in order to identify forestland changes over time. Historical and current digital orthoimagery is generated through photogrammetric operations. Forestlands are delineated in a spatiotemporal environment; state property rights in forestlands are allocated and land ownership is established within the framework of HCP. This paper demonstrates that the integration of airborne remote sensing and collateral data with a GIS is an effective approach for cadastral forest mapping. The produced GIS databases and large-scale Forest Maps may serve as a data foundation towards a land register of forests. [ABSTRACT FROM AUTHOR]

The Grove Mountains Area is situated to the southwest of Princess Elizabeth Land, the inland areas of east Antarctica. Adopting Landsat-4 TM images, a coloured satellite image map of Grove Mountains was produced and used to design an expedition route to Grove Mountains in 1998. In 2000, a field survey of the core area was completed with GPS and total station surveys. However, traditional mapping methods are no longer the most efficient means of obtaining topographic maps or DEM in large areas, especially those areas that are more inaccessible. Synthetic aperture radar interferometry has been proposed as a potential technique for digital elevation model (DEM) generation, topographic mapping, and surface motion detection. This paper presents an experimental investigation of the ERS-½ SAR tandem data on DEM generation of the Grove Mountains Core Area, and compares the DEM derived by INSAR with the DEM generated using field survey data. The paper confirms that INSAR is a very valuable technique to be utilized in Antarctica, and that INSAR can be employed to produce complementary field survey products. [ABSTRACT FROM AUTHOR]

The article discusses the state of mapping in remote areas around the world. According to the author, many areas of the world have been geodetically surveyed and topographically mapped using traditional techniques, but major European powers have modified their techniques, such as aerial photography and radio time service. He believes that orbital imagery is far superior in geometric fidelity than existing paper maps or digital data derived from digitized paper maps.

False matches in tie-point image matching are common. This paper introduces a straightforward and effective prepossessing method to reject false matches from initial matches. The method is based on the idea of Hough transform using only two geometrical consistency parameters, namely, the scale parameter and the rotation parameter between two images. A weighted voting strategy is employed, and it can further improve the robustness of the algorithm. The method can handle a large rate of outliers and produce more robust matches with low complexity. No assumptions with regard to the relative pose between two images are necessary, and large perspective deformation can be handled as well. Experiments with ground reference data show that the algorithm works effectively even when the ratio of inliers is below 10 percent. In these data, the ratio of inliers can be improved from 5 percent to 40 percent on average. [ABSTRACT FROM AUTHOR]

The article focuses on the 61-years of John "Jack" Sherman of Robinson Aerial Surveys Inc. (RAS) who worked with aerial photogrammetry. Topics discussed include the establishment of the company by civil engineer C. S. Robinson in Ithaca, New York, the use of aerial photography for engineering design, survey, and general mapping projects, and he uses the Low Altitude Mapping Photography (LAMP), a helicopter based camera system to improve the photogrammetric process.

For most of the mapping and engineering survey projects, the heights are still required above Mean Sea Level (MSL), whereas the heights given by Global Positioning System (GPS) are in terms of the WGS84 ellipsoid. The heights above MSL are called Orthometric Heights. Presently for India and many other countries, who have not yet established their own geoid, there is no method to convert the ellipsoidal heights to Orthometric heights. For such countries, the only way available to obtain Orthometric heights is through establishing a dense network of leveling lines that is very time consuming, cumbersome, and manpower- and cost-intensive. An alternative solution has been proposed that requires only a very few ground control points to be provided by GPS and a skeletal leveling network for large scale photogrammetric mapping without compromising accuracy. The concept has been validated with the aid of two test areas. It is shown that by adopting the proposed method, there is a significant reduction in the required number of GPS control points by more than 55 percent and the number of MSL height control points (as also the preprocessing/postpointing effort) by as much as 90 percent. [ABSTRACT FROM AUTHOR]

Images with large-area repetitive texture, significant viewpoint and illumination changes as well as occlusions often induce high-percentage keypoint mismatches, affecting the performance of vision-based mapping and navigation. Traditional methods for mismatch elimination tend to fail when the percentage of mismatches is high. In order to remove mismatches effectively, a new geometry-based approach is proposed in this paper, where Geometric Correspondence Feature (GCF) is used to represent the tentative correspondence. Based on the clustering property of GCFs from correct matches, a new clustering algorithm is developed to identify the cluster formed by the correct matches. With the defined quality factor calculated from the identified cluster, a Progressive Sample Consensus (PROSAC) process integrated with hyperplane-model is employed to further eliminate mismatches. Extensive experiments based on both simulated and real images in indoor and outdoor environments have demonstrated that the proposed approach can significantly improve the performance of mismatch elimination in the presence of high-percentage mismatches. [ABSTRACT FROM AUTHOR]

The main obstacle to achieving high precision in georeferencing from high-resolution satellite imagery (HRsI) remains the need for provision of good quality ground control points (GCPS), whether the GCP5 are used to remove biases in RPC triangulation or to support physical sensor orientation models. The provision of GCP5 can be very costly and is often not feasible in remote regions, the very areas where mapping from satellite imagery shows significant potential. In order to drastically reduce the number of GCP5 required for georeferencing from HRSI, a generic sensor orientation model incorporating strip adjustment capability has been adopted. Under this approach, the metadata for each separate scene is merged to produce a single, continuous set of orbit and attitude parameters, such that the entire strip of tens of images can be treated as a single image. The merging of orbit data results in a considerable reduction in both the number of unknown parameters and the number of required GCP5 in the sensor orientation. RPC5 are then generated from the adjusted orientation data for each image forming the strip or block. Application of the method to very long strips of ALOS PRSIM imagery is reported in this paper. The results of experimental testing indicate that one-pixel level accuracy can be achieved over strip lengths of more than 50 ALOS images, or 1,500 km. with as few as four GGPs. [ABSTRACT FROM AUTHOR]

Important considerations for large scale mapping from satellite images are information content and geometric fidelity. Cartosat series of satellites with stereo mapping capability have become the mainstay towards large scale mapping for urban and rural applications. Algorithms for processing of high-resolution Indian remote sensing satellite data has been developed at ADRIN and is used for operational generation of data products. Variations in the sensor model with respect to the viewing geometries of Cartosat-1 and Cartosat-2 are explained in the paper. Finally, an assessment of the mapping potential of the satellites is discussed. The geometric accuracy achieved from Cartosat-1 and Cartosat-2 images over the same checkpoints are compared. DEM, geometric accuracy, and capability for topographic feature capture are good enough for making 1:10 000 and 1:7 000 scale maps from Cartosat-1 and Cartosat-2, respectively. Based on the error estimation and analysis, it is concluded that if the strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the generation and update of topographic maps of scale 1:10 000 and larger. [ABSTRACT FROM AUTHOR]

This paper examines the positional accuracy of the declassified KH-9 Hexagon imagery and derived DEM. Aimed at geodesy and mapmaking, the KH-9 program (1973 to 1980) resulted in an image archive with world- wide stereo coverage at 6 to 9 m. We used six KH-9 images acquired in 1980 over two testfields in Central Asia. Using reseau marks on the scanned KH-9 frames, we found and corrected image distortions. In bundle orientation with Ground Control Points (GCPs) from QuickBird images, we achieved horizontal accuracies below 6 m for a fiat terrain testfield and approximately 10 m for a mountainous terrain testfield. With three GCPs the image orientation horizontal accuracy degraded by only 20 percent. We generated a DEM from the KH-9 images and estimated its vertical accuracy using IceSAT laser altimetry data and an additional DEM from 1:25 000 topographic maps. The DEM RMSE was 6.18 m over flat terrain and 20.0 m over mountainous terrain. [ABSTRACT FROM AUTHOR]

In this paper, cadastre agricultural cartography was enriched using crop raster maps obtained from remote sensing images. The work demonstrates the implications of applying two new terms: fidelity and purity. Per-pixel classifications and polygon enrichments were compared taking into account: (a) the consequences of using a more or less conservative strategy at the classification stage, using fidelity, and (b) the consequences of using modal thresholds at the enrichment stage when deciding which category each polygon is to be assigned to, using purity. More than 300,000 pixels and 2,800 polygons were used to measure the thematic accuracy of ten agricultural categories by means of confusion matrices. These were computed at pixel, polygon, and area level. Thematic accuracy was calculated in the classical way and without taking into account unclassified pixels as errors, as well as by paying special attention to the consequences for the classified area. The results show that polygon enrichment is a useful methodology, achieving thematic accuracies of 95.6 percent, when optimum parameters are used, while classifying 87.4 percent of the area. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imaging can be a feasible alternative or a complement to traditional optical remote sensing techniques because it does not depend on solar illumination and weather conditions. The high spatial resolution of SAR, such as the Intermap STAR-3i airborne SAR image with 1.25 m spatial resolution, makes it applicable for high spatial resolution mapping purposes. However, difficulties sometimes exist in the interpretation of SAR images. Image fusion presents an alternative to improve the interpretability of SAR images by fusing the color information from moderate spatial resolution multispectral (Ms) images. In this paper, a new fusion method based on the integration of wavelet transform and IHS (Intensity, Hue, and Saturation) transform is proposed for SAR and MS fusion to maintain the spectral content of the original MS image while retaining the spatial detail of the high-resolution SAR image. Three data sets are used to evaluate the proposed fusion method: two sets are airborne SAR images with MS images at different spatial resolutions; the other set is a Radarsat image with a Landsat TM image. The fusion results are evaluated visually and statistically. The evaluation shows that successful results are achieved in the fusion of all SAR and MS images from a variety of sensors with significant spatial and spectral variations by using the proposed image fusion method. [ABSTRACT FROM AUTHOR]

Coastal zone mapping, particularly of shorelines, is critical for safe navigation, resource management, environmental protection, and sustainable coastal development. This paper explains a method for extracting coastal blufflines where airborne lidar (Light Detection and Ranging) data is integrated with orthoimages. A historical Lake Erie bluff top line was used as a reference line and a series of transects created perpendicular to it. After three-dimensional elevation profiles of these transects were obtained from a lidar DSM (digital surface model), a new algorithm was used to extract from these transects initial points identifying bluff top and toe. These points were connected to form an initial bluffline. The horizontal position of this initial bluffline was refined with edges obtained from the orthoimages using techniques including mean-shift segmentation, surface reconstruction, and edge detection. Results show this method is capable of deriving blufflines having a similar quality to that from manual digitization. [ABSTRACT FROM AUTHOR]

Empirical research in DEM accuracy assessment has observed that DEM errors are correlated with terrain morphology, sampling density, and interpolation method. However, theoretical reasons for these correlations have not been accounted for. This paper introduces approximation theory adapted from computational science as a new framework to assess the accuracy of DEMs interpolated from topographic maps. By perceiving DEM generation as a piecewise polynomial simulation of the unknown terrain, the overall accuracy of a DEM is described by the maximum error at any DEM point. Three linear polynomial interpolation methods are examined, namely linear interpolation in ID, TIN interpolation, and bilinear interpolation in a rectangle. Their propagation error and interpolation error, whose sum is the total error at a DEM point, are derived. Based on the results, the theoretical basis for the correlation between DEM error and terrain morphology and source data density is articulated for the first time. [ABSTRACT FROM AUTHOR]

Much effort has been spent on examining the spatial variation of classification accuracy and associated factors on a per-pixel basis. In the past few years, object-based classification has attracted growing interest. This paper examines factors affecting the spatial variation of classification uncertainty in an object-based vegetation mapping. We studied six categories of factors in an object-based classification: general membership, topography, sample object density, spatial composition, sample object reliability, and object features. First, classification uncertainty (classification accuracy on a per-case basis) is derived with a bootstrap method. Then, six categories of factors are quantified by categorical or continuous variables. In this step, the appropriate radius for calculating the spatial composition metrics of sample objects is also discussed. Finally, classification uncertainty is modeled as a function of those factors using a mixed linear model. The significant factors are identified and their parameters are estimated from restricted maximum likelihood fit. The modeling results show that elevation, sample object size, sample object reliability, sample object density, and sample spatial composition significantly influence the object-based classification uncertainty. Many of these factors are closely related to the object-based approach. The result of this study helps in understanding classification errors and suggests further improvement of the classification. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic and fast algorithm for registering aerial image sequences to vector map data using linear features as control information. Our method is based on the extraction of linear features using active contour models (also known as, snakes), followed by the construction of a polygonal template upon which a matching process is applied. To accommodate more robust matching, this work presents both exact and inexact matching schemes for linear features. Additionally, in order to overcome the influence of the snakes-based extraction process on the matching results, a matching refinement process is suggested. Using the information derived from the matching process, we then determine the transformation parameters between overlapping images and generate a mosaic image sequence, which can then be registered to a map. The performance of the proposed scheme was tested on sequences of aerial imagery of 1 m resolution that were subjected to shifts and rotations using both the exact and inexact matching scheme, and was shown to produce angular accuracy of less than 0.7 degrees and positional accuracy of less than two pixels. [ABSTRACT FROM AUTHOR]

High-resolution imaging satellites are a valuable and cost effective data acquisition tool for a variety of mapping and GIS applications such as topographic mapping, map updating, orthophoto generation, environmental monitoring, and change detection. Sensor modeling that describes the mathematical relationship between corresponding scene and object coordinates is a prerequisite procedure prior to manipulating the acquired imagery from such systems for mapping purposes. Rigorous and approximate sensor models are the two alternatives for describing the mathematics of the involved imaging process. The former explicitly involves the internal and external characteristics of the imaging sensor to faithfully represent the geometry of the scene formation. On the other hand, approximate modeling can be divided into two categories. The first category simplifies the rigorous model after making some assumptions about the system's trajectory and/or object space. Gupta and Hartley's model, parallel projection, self-calibrating direct linear transformation, and modified parallel projection are examples of this category. Other approximate models are based on empirical formulation of the scene-to-ground mathematical relationship. This category includes among others, the well-known Rational Function Model (RFM). This paper addresses several aspects of sensor modeling. Namely, it deals with the expected accuracy from rigorous modeling of imaging satellites as it relates to the number of available ground control points, comparative analysis of approximate and rigorous sensor models, robustness of the reconstruction process against biases in the available sensor characteristics, and impact of incorporating multi-source imagery in a single triangulation mechanism. Following a brief theoretical back- ground, these issues will be presented through experimental results from real datasets captured by satellite and aerial imaging platforms. [ABSTRACT FROM AUTHOR]

Super-resolution or sub-pixel class mapping is the task of providing fine spatial resolution maps of, for example, landcover classes, from satellite sensor measurements obtained at a coarser spatial resolution. Often, the only information available consists of coarse class fraction data, typically obtained through spectral unmixing. This paper shows how to integrate, in addition to such coarse fractions, class labels at a set of fine pixels obtained independent of the satellite sensor measurements. The integration of such fine spatial resolution information is achieved within the Indicator Kriging formalism in either a prediction or simulation mode. The spatial dissimilarity or texture of class labels at the fine (target) resolution is quantified in a non-parametric way from an analog scene using a set of experimental indicator semivariogram maps. The output of the proposed procedure consists of maps of probabilities of class occurrence, or of a series of simulated class maps characterizing the inherent spatial uncertainty in the super-resolution mapping process. [ABSTRACT FROM AUTHOR]

The traditional 3 × 3 cell neighborhood used in a focal operation on a raster layer has a square shape that results in a dimensional neighborhood of which the orientation is eventually arbitrary to the physical features represented. This paper presents an experiment using a circular neighborhood to calculate slope gradient. Comparisons of the results from a circular neighborhood with the results from some traditional methods show that (a) for a smooth surface, the result from a circular neighborhood is more accurate than that from a square neighborhood, (b) a circular neighborhood is generally more sensitive to noise in the input DEM than a square neighborhood, and (c) in a validation using field measurements, the circular neighborhood performs better than the square neighborhood when the ratio of user-specified neighborhood size to cell size is high. [ABSTRACT FROM AUTHOR]

Integrated multi-sensor systems, with their major progress in terms of sensor resolution, data rate and operational flexibility, have become a very attractive mapping tool over the last decade. In aerial mapping application, for example, Exterior Orientation (EO) parameters for the imaging sensors are required. Using the integrated Differential Global Positioning System (DGPS) with Inertial Measurement Units (IMU), direct determination of the EO parameters can be obtained from the integrated system navigation solution. This process is referred to as Direct Georeferening (DG). DG provides substantial benefits over the in direct determination method of estimating the EO parameters from conventional Aerial Triangulation (AT) techniques using a block of images with a sufficient number of known control points. These benefits include the ability to map remote and inaccessible regions, and by replacing tie point measurements/matching and AT, significant cost-savings can be obtained for projects that do not require stereo models (such as projects with existing DEM or single image). The accuracy of DG, however, is limited by the accuracy attainable by the DGPS and any residual datum calibration errors. These can typically be as large as 10 cm RMSE, which is not sufficient for some large scale mapping applications. However, by combining the direct EO data a traditional block adjustment, AT techniques can be used to remove the residual errors in the solution. This technique is known as Integrated Sensor Orientation (ISO). It has several advantages over traditional AT, primarily because the stable geometry provided by direct EO can reduce the number of required GCP and tie-point to a minimum. At the same time, ISO provides an excellent tool for Quality Control/Quality Assurance (QC/QA) of the EO derived from a DG system. This paper examines the factors that determine the system performance for ISO. In addition, examples are given to illustrate the expected accuracy of an aerial mapping project using ISO under different qualities of DGPS/IMU data. [ABSTRACT FROM AUTHOR]

This paper presents the potential of integrating radar data features with optical data to improve automatic land-cover mapping. For our study area of St. Louis, Missouri, Landsat ETM+ and Radarsat images are orthorectified and co-registered to each other. A maximum likelihood classifier is utilized to determine different land-cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. In addition, the Landsat images are fused with several combinations of processed radar features. The classification accuracies from the Landsat and radar feature combinations are studied. Our research finds that fusion of multi-sensor data improves the classification accuracy over a single Landsat sensor, although different processing techniques on radar images are required to obtain the best results. In our study, fusion of Landsat images and Radarsat feature combinations from a 13 × 13 entropy window, 9 × 9 data range widow, and 19 × 19 mean filter window achieves the highest overall accuracy improvement (10 percent) over the Landsat images alone. [ABSTRACT FROM AUTHOR]

This paper reports on mapping procedures developed by the U.S. Geological Survey that use Viking Orbiter imagery and Mars Orbiter Laser Altimeter (MOLA) derived radii to produce topographic data. The use of Mosaiced Digital Image Models (MDIMs), created from Viking Orbiter images, and MOLA data to provide horizontal and vertical control is reviewed. We describe procedures to adapt a commercial digital photogrammetric workstation to work with planetary data. [ABSTRACT FROM AUTHOR]

The High Resolution Stereo Camera (HRSC) on the European spacecraft Mars Express is the first camera on a planetary mission especially designed for photogrammetric and cartographic purposes. Since January 2004 the camera has been taking image data from the Martian surface, characterized by high-resolution, stereo capability and color. These data provide an enormous potential for the generation of 3D surface models, color orthoimages, topographic and thematic maps, and additional products. The image data acquired undergo calibration and systematic processing to orthoimages and 3D data products. Within the international HRSG Science Team the members of the Photogrammetric/Cartographic Working Group are concerned with further refinements in order to achieve highest quality data products. These activities comprise improvements of the exterior orientation of the camera, various approaches to enhance DTM quality, and the generation of maps in the standard scale of 1:200 000 and larger scales as well. The paper reports on these activities and the results achieved so far. [ABSTRACT FROM AUTHOR]

A high-precision land-based integrated mapping system has been developed at The Ohio State University to support road centerline mapping operations at the Ohio Department of Transportation District 1 Office. The system represents a transition from the traditional mobile mapping paradigm towards a highly automated and autonomous design following the trends of modern geoinformatics. The two key components of the custom-designed system are a high-precision integrated GPS/INS navigation system and a fully digital and automated imaging subsystem. The van-based mapping system was designed to deliver the road centerline positions at subdecimeter accuracy in a highly automated manner with limited human interaction in near real time. The paper presents the system's concept and design, followed by an individual performance evaluation of the navigation and imaging components; and finally road test results, representing an operational environment, are also reported. [ABSTRACT FROM AUTHOR]

The demands for ground digital elevation models (DEMs) have been increasing rapidly. Many modern mapping technologies, such as lidar scanning, interferometric SAR (IfSAR), and stereo image matching, are capable of generating digital surface models (DSMs) directly. Research on deriving DEMs from DSMs is a topical area. Airborne IfSAR technology has been in commercial operation since 1996. However, it is still technically challenging to automate the process of generating DEMs from IfSAR DSMS. The lack of scientific studies in this area is mainly caused by the sensitivity and availability of commercial IfSAR data. In this study, researchers have been working with the commercial IfSAR system developer to perform an initial study on automatic reconstruction of ground OEMs from IfSAR DSMs. This paper presents a compact and yet efficient version of our developed approach. This approach has been tested by using many IfSAR DSMs in different hilly urban areas with light and moderate variations in relief. An accuracy analysis of the reconstructed ground DEMs against the laser DSMs and DEMS has also been performed to test the reliability and efficiency of our approach. [ABSTRACT FROM AUTHOR]

This paper describes land-cover data as part of a broad management and analytical infrastructure. This land-cover data infrastructure provides a flexible and inclusive mechanism for a full sequence of analyses of land-cover, including description, management, analysis, and modeling of land-cover change dynamics. The land-cover data infrastructure addresses the diversity of description and application of landcover data, the sources of land-cover data, and the sequence of processing for land-cover mapping, analysis, and modeling. The design of the infrastructure produces an integrated suite of databases and processing toolboxes that supports this entire processing stream and that focuses on the data needs of the applications of land-cover data, rather than the representation of a cartographic land-cover product. The infrastructure integrates diverse data sources, including imagery, field data, and existing land-cover maps with data processing operations; the goal is to provide data for modeling and analysis of landcover dynamics relevant to application needs of different users. The opportunity for further development of GIS, remote sensing, and spatial analysis to be used to conduct rigorous scientific investigation of land-cover change is discussed in the context of the data infrastructure. [ABSTRACT FROM AUTHOR]

Efficient irrigation system management requires accurate location and condition information for system components. Bureau of Indian Affairs (BIA) irrigation managers currently work with outdated paper manuscripts or minimally attributed digital datasets. This paper examines how the Global Positioning System (GPS), a geographic information system (GIS), and digital cameras were used to perform a detailed inventory of three irrigation units managed by the BIA on the Blackfeet Indian Reservation, Montana. More than 500 digital pictures were captured on approximately 500 kilometers (315 miles) of linear structures. Over 2,100 point structures were inventoried. The resultant dataset provides vastly improved analysis and display capabilities. Managers can identify and locate system components attributed as 'repair immediately' and simultaneously view digital pictures of the irrigation system components, along with associated attributes and data components.This greatly facilitates their ability to estimate repair cost and complexity, as well as to determine materials needed to accomplish therepairs. The digital and photographIic data thus become a 'living' dataset into which updates can be easily incorporated. [ABSTRACT FROM AUTHOR]