ID: 57412
Title: Cell-based automatic deformation computation by analyzing terrestrial Lidar point clouds
Author: Jing Wu, Pierre-Yves Gillieron, and Bertrand Merminod
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No4, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Deformation map, Epochs
Abstract: This paper presents a cell-based approach for computing the deformation of a monitored object by analyzing point cloud data from terrestrial lidar. This approach can automatically generate an informative deformation description (called "deformation map") with distinctive deformation characteristics for different partial areas. The approach consists of three major computing steps: (a) "split"- the space of the monitored object is divided into 3D uniform cells, (b) "detect" - deformation parameters for each cell (called "meta-deformation") are estimated by comparing the point clouds in the cell sampled at Epochs I and II, and (c) "merge"- the adjacent cells with similar meta-deformation are combined together in a partial area with a consistent "sub-deformation". The main contributions of this paper are: (a) a hybrid deformation model for incremental and comprehensive deformation representation, including metadeformation, sub-deformation and deformation map, (b) a systematic procedure of "split - detec-merge" to automatically and gradually estimate the hybrid deformation model, and (c) a complete validation with a couple of synthetic and real datasets.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57411
Title: Laser scanning in heritage documentation: The scanning pipeline and its challenges
Author: Heinz Ruther, Roshan Bhurtha, Christoph Held, Ralph Schroder, and Stephen Wessels
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No4, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Heritage sites, 3D landscape models
Abstract: In an attempt to show the complexity of terrestrial laser scanning of heritage sites and dispel the not infrequently found perception of a largely automated tool with close to real time processing capabilities, the individual steps of the data acquisition and processing pipelines, including registration, point cloud cleaning, surface reconstruction, hole filing, texturing and final output are discussed. Emphasized are areas in which further development and research are desirable. A brief discussion of the concepts and implementation of the African Cultural Heritage Sites and landscapes Project, as executed by the Zamani Research Group at the University of Cape town, is incorporated into the paper. This database integrates spatial and non-spatial data and focuses on architectural heritage sites and cultural land-scapes. The spatial data comprises of 3D laser scans, GIS ' s of each site and its environment, stereo images, panoramas, ground plans, elevations and sections derived from laser scan methods, contextual photography and videos as well as 3D landscape models. The database is primarily designed as a resource for research and higher education. However, the spatial data acquired for the project are presently used in a number of restoration and conservation projects. An important further objective is the creation of a permanent digital record. The project, funded by the Andrew W. Mellon Foundation, is a joint initiative of the Zamani Research Group at the University of Cape Town and JStor, New York.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57410
Title: Photogrammetric monitoring of the construction of a solar energy dish concentrator
Author: M R Shortis and G Burgess
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: solar concentrator, Photogrammetry
Abstract: Close-range photogrammetry has been used to control the construction of a jig used in the assembly of a new generation solar concentrator and the validation of the final, mirrored surface. The concentrator is a concave paraboloid dish with an area of a 489 m2. The typical photogrammetric relative precision realized was 1:175,000, corresponding to an accuracy of better than 1 mm at the jig reference points. The validation of the mirrored surface achieved a relative precision of 1:300,000. Photogrammetry was also used to characterize the dish mirror panels. The rear surface of the panels was mapped, as it made possible a denser target array and quicker image capture than if the reflective surface was used. The targets were produced with a digital projector, and the typical measurements precision attained was 1:150,000. This paper describes the equipment and techniques required to provide the required accuracy and precision for the jig and pane surfaces.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57409
Title: Network orientation using the scaled orthographic projection for parameter initialization
Author: Keith F Blonquist and Robert T Pack
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Bundle adjustment, scaled orthographic projection
Abstract: Bundle adjustment is a well known and reliable method used in photogrammetry for image network orientation which requires relatively accurate initial approximations of image orientations and point coordinates. The initialization problem has proved difficult and a variety of initialization techniques have been proposed. We present a new method that takes advantage of the scaled orthographic projection to compute direct inear solutions that approximate the image network geometry. The algorithm includes two relative orientation methods adapted from previous work, and incorporates a recently developed orthographic bundle adjustment method. Following initial network orientation, image coordinates are corrected for perspective to obtain an intermediate solution which is converted into perspective projection parameters and a final bundle adjustment is performed. The method has been tested using several image sets and has proven to be effective at various fields of view, with a variety of imaging network geometries, and with different object point geometries.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57408
Title: Estimating urban leaf area index (LAI) of individual trees with hyperspectral data
Author: Ryan R Jensen, Perry J Hardin, and Andrew J Hardin
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Leaf area index (LAI), multiple regression models
Abstract: This study estimated leaf area index (LAI) of individual urban trees as a function of spectral features derived from airborne hyperspectral data. Candidate features included spectral indexes, principal components, and calibrated reflectance values. Hyperspectral images were acquired over Provo, Utah area, and LAI of 204 deciduous trees was measured in the field. These tree canopies were identified on the images, and spectral features were extracted using both whole canopy and mean-lit spectra techniques. Multiple regression and artifical neural networks were used to model leaf area and determine which spectral features were most strongly related to it. Results established that simple hyperspectral vegetation indexes explained that simple hyperspectral vegetation indexes explained more variation in urban tree LAI than either principal component scores or simple band reflectance values. The neural network model trained with a subset of those indexes explained more variation in LAI (R2 = 64.8 percent) than any of the multiple regression models tested.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57407
Title: Comparing the performance of empirical, semi-empirical, and curve fitting models in predicting cyanobacterial pigments
Author: A L Nguy-Robertson, L. Li, L. Tedesco, J Wilson and E Soyeux
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Chlorophyll a (CHL), phycocyanin (PC), Modified Gaussian model (MGM)
Abstract: This study presents a comparative analysis of several algorithms for the estimation of cyanobacterial pigments chlorophyll a (CHL) and phycocyanin (PC) from hyperspectral reflectance, while also providing a consistent basis for determining the best performing models in predicting both CHL and PC concentrations. Simple band ratio algorithms, band tuning methods, semi-empirical algorithms, and the modified Gaussian model (MGM) parameters were used to estimate CHL and PC from multiple source spectral datasets of two eutrophic central Indiana reservoirs: Eagle Creek and Morse. The spectral datasets were collected over a three-year period (2005 to 2007) using two field-based (ASD Field - Spec; Ocean Optics USB4000) spectroradiometers Spectral parameters used in these mapping algorithms were examined for their correlation to the CHL and PC concentrations. The results demonstrate for estimating CHL, simple and modified band ratios performed well; for PC estimation, the highest performing models inlude the algorithms using MGM strength and band tuning methodology.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57406
Title: Urban tree cover mapping with relief-corrected aerial imagery and Lidar
Author: Brad Lehrbass and Jinfei Wang
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Tree canopy, ormalized Digital Surface Model (NDSM)
Abstract: Urban tree canopy cover is often mapped by classifying high-resolution multispectral imagery. However, it can be difficult to differentiate low-lying vegetation from tree cover using optical data alone. Combining a lidar-derived Normalized Digital Surface Model (NDSM) improves classification accuracy, but the optical imagery is often imperfectly aligned with the NDSM. Aerial imagery is normally orthorectified using the ground elevation. However, tall objects in the orthorectified imagery still suffer from relief displacement. This can cause classification errors when lidar and the aerial imagery are combined.
This study presents an approach for urban tree cover mapping composed of two parts: a method for correcting the relief displacement of trees in previously orthorectified aerial imagery, and an object - based classification method which combines relief-corrected multispectral aerial imagery with a lidar-derived NDSM. Using these methods, the tree cover was mapped for a 1,600 ha region of London, Ontario, Canada with improved positional and calssification accuracy.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57405
Title: Identification of waste tires using high-resolution multispectral satellite imagery
Author: Becky Lauren Quinlan and Patricia G Foschi
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 5, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Waste tires, QuickBird imagery
Abstract: Regulatory agencies have identified unregulated stockpiles of waste tires as a threat to human health and environmental safety. Waste tires are complex spectrally-variable targets, mixing geographically and spectrally with other land covers like water and shadow. Additionally, few training sets are available. We describe a methodology combining visual anaysis and the use of a univariate decision tree to locate waste tire piles in 8,498 sq km of QuickBird imagery along the United States-Mexico border. A total of 89 sites were identified as possible waste tire disposal sites and targeted for field inspection. Excluding two sites previously known to the analyst and eleven sites that were inaccessible to the inspectors, 66 of target sites (87 percent) were correctly identified by this methodology as containing tires. Three sites were determined to be false positives (4 percent errors of commission) through ground verification, but errors of omission were not able to be determined in this study.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57404
Title: Cluster and classification techniques for the Biosciences
Author: None
Editor: Alan H Fielding
Year: 2007
Publisher: Cambridge University Press, 2007
Source: Centre for Ecological Sciences
Reference: None
Subject: Cluster and classification techniques for the Biosciences
Keywords: None
Abstract: None
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57403
Title: Hydrology and Watershed Services in the Western Ghats of India: Effects of Land use and Land cover change
Author: None
Editor: Jagdish Krishnaswamy, Sharachchandra Lele and R Jayakumar
Year: 2006
Publisher: Tata McGraw-Hill Publishing Company Limited, 2006
Source: Centre for Ecological Sciences
Reference: None
Subject: Hydrology and Watershed Services in the Western Ghats of India: Effects of Land use and Land cover change
Keywords: None
Abstract: None
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57402
Title: Towards operational automatic flood detection using EOS/MODIS data
Author: Donglian Sun, Yunyue Yu, Rui Zhang, Sanmei Li and Mitchell D Goldberg
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 6, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Moderate-Resolution Imaging Spectroradiometer (MODIS), Regression Tree (RT), Thematic Mapper (TM)
Abstract: This study investigates how to derive water fraction and flood map from the Moderate-Resolution Imaging Spectroradiometer (MODIS) using a Regression Tree (RT) approach, which can integrate all predictors. The New Orleans, Louisiana floods in August 2005 were selected as a case study. MODIS surface reflectance with matched water fraction data were used for training. The tree-based regression models were obtained automatically through learning process. The tree structure reveals that near-infrared reflectance is more important than the difference and ratio between near-infrared and visible channels for water fraction estimate. Flood distributions were generated using the differences in water fraction values between after and before the flooding. The derived water fractions were evaluated against 30 m Thematic Mapper (TM) data from Landsat observations. Water fractions derived from the MODIS and TM data agree well (R2 = 0.94, bias = 0.38 percent, and RMSE = 4.35 percent). The results show that the RT approach in dynamic monitoring of floods is acceptable.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57401
Title: Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: A cognitive approach
Author: Sarah E Battersby, Michael E Hodgson and Jiayu Wang
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 6, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: Disaster, emergency management operations
Abstract: In disaster response, timely collection and exploitation of remotely sensed imagery is of increasing importance. Image exploitation approaches during the immediate (first few days) aftermath of a disaster are predominantly through visual analysis rather than automated classification methods. While the temporal needs for obtaining the imager are fairly clear (within a one- to three-day window), there have only been educated guesses about the spatial resolution requirements necessary for the imagery for visual analysis. In this paper, we report results from an empirical study to identify the coarsest spatial resolution that is adequate for tasks required immediately following a major disaster. The study was conducted using cognitive science experimental methods and evaluated the performance of individuals with varying image interpretation skills in the task of mapping hurricane-related residential structural damage. Through this study, we found 1.5 m as a threshold for the coarsest spatial resolution imagery that can successfully be used for this task. The results of the study are discussed in terms of the likelihood of collection of this type of imagery within the temporal window required for emergency management operations.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57400
Title: Error propagation in Raster data integration: impacts on landscape composition and configuration
Author: Zachary J Christman and John Rogan
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 6, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: thematic classes, raster-based transformation methods
Abstract: Integrating raster-based categorical maps from multiple sources necessitates the transformation of geometric characteristics to compare maps, as in land change analyses. By projecting maps to a new geographic reference framework and scaling pixel values to a new size, distortions of map information are introduced that can affect the proportion and arrangement of thematic classes across the landscape. Using a sample land cover dataset depicting a heterogeneous landscape, this paper examines these impacts using three common raster-based transformation methods and introduces a new vector-based method that minimizes error propagation. While relative class area was best preserved by a nearest-neighbor resampling method, distortions to the contiguity of thematic classes and the overall fragmentation of the landscape were minimized when using the vector-based projection and resampling method. Results demonstrate that more than a third of pixel values of a categorical map may be affected by common projection and scaling methods and reinforce the need for careful attention to impacts of error propagation in categorical data transformations.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57399
Title: Neuro-fuzzy classification of submarine lava flow morphology
Author: J Timothy McClinton, Scott M White, and John M Sinton
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 6, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: neuro-fuzzy method, Kappa coefficient, Galapagos Spreading Center (GSC),
Abstract: This paper presents a novel approach to semi-automated classification of volcanic morpholgoy on the seafloor using high-resolution multibeam sonar bathymetry and side-scan sonar backscatter imagery. The classification methodology combines a fuzzy inference system and neural network theory in an adaptive neuro-fuzzy inference system (ANFIS) and is capable of rapidly classifying submarine lava morphology based on bathymetry-derived surface geometry and backscatter-derived attributes of acoustics and texture. The system has been applied to a study area on a seafloor spreading ridge, the Galapagos Spreading Center (GSC), in order to quantify the distribution and relative abundance of lava flow types, which can be used to indicate variations in eruption and emplacement dynamics. A detailed assessment shows the classification has an overall accuracy of almost 90 percent with a Kappa coefficient of 0.84. The neuro-fuzzy method described here is shown to be an efficient and reliable tool for classification of submarine lava morphology.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None
ID: 57398
Title: A framework for supervised image classification with incomplete training samples
Author: Qinghua Guo, Wenkai Li, Desheng Liu and Jin Chen
Editor: Russell G Congalton
Year: 2012
Publisher: ASPRS, Vol 78, No 6, June 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Photogrammetric Engineering and Remote Sensing
Keywords: one-by-one (OBO) classification,
Abstract: For traditional supervised classification methods, all land cover types need to be exhaustively labeled to train the classifier. However, there are situations where the training sample classes are incomplete due to a lack of understanding of ground cover types in the image. In this study we propose a one-by-one (OBO) classification framework to address this incomplete training sample problem. The OBO approach is based on a one-class classifier (positive and unlabeled learning algorithm), and it extracts the land-cover type from the image one at a time. The performance of the proposed method was compared with a traditional supervised classifier using a high spatial resolution image. The average accuracy of the new method is 76.34 percent across different training sample sizes, whereas the accuracy of the classical approach is 66.46 percent, with an increase of 9.88 percent. The results demonstrate that the proposed new framework provides significantly higher classification accuracy than the classical approach at the 95 percent confidence level, and shows promise in dealing with the incomplete training sample problem for supervised image classification.
Location: TE 12, Biologicalsciences Building, IISc
Literature cited 1: None
Literature cited 2: None