ID: 55342
Title: Building footprint database improvement for 3D reconstruction: A split and merge approach and its evaluation
Author: Bruno Vallet, Marc Pierrot-Deseilligny, Didier Boldo, Mathieu Bredif
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Photogrammetry, segmentation, reconstruction, urban scene, building
Abstract: We present a general framework to improve a vectorial building footprint databse consisting of a set of 2D polygons. The aim of this improvement is to make the database more proper to subsequent 3D building reconstruction at a large scale. Each polygon is split into several simple polygons guided by a digital elevation model (DEM). We say that this segmentation is vectorial as we produce segmentations that intrinsically have simple polygonal shapes, instead of doing a raste segmentation of the DEM within the polygon then trying to simplify it in a vectorization step. The method is based on a Mumford and Shah like energy functional characterizing the quality of the segmentation. We simplify the problem by imposing that the segmentation edges have directions present in the input polygon over which the DEM is defined. We evaluate the validity of the proposed method on a very large dataset and discuss its pros and cons based on this evaluation.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55341
Title: Statistical analysis of signal measurement in time-of-flight cameras
Author: Faisal Mufti, Robert Mahony
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: 3D time-of-flight camera, Statistical analysis, SNR, maximum likelihood, Range error analysis
Abstract: Three-dimensional imaging systems have evolved significantly in the last two decades due to increase in demand for tasks in the field of close range photogrammetry. The fast and growing need of 3D imaging devices has given rise to range image technology, especially time-of-flight (TOF) cameras, that provide direct measurement of distance between the camera and the targeted surface. A significant advantage of TOF devices over traditional range data sensors is their capability to provide frame rate range data over a full image array. In phase shift TOF cameras, phase shift sampling of the received signal is used to measure amplitude, phase and the offset (intesity) of the received signal. As a result, the quality of the measurement of these sensors depends heavily on signal-to-noise (SNR) of the incomign signal and the subsequent processing algorithms. A detailed understanding of the statistical distributions of the measurement parameters is crucial for accurate distance measurement analysis especially in low SNR scenarios. In this paper, we provide explicit noise models for the three parameters of amplitude, phase and intensity. The proposed stochastic model helps in investigating the effect of noise on signal and classifying range data reliability in TOF cameras. The model is used for prediction of errors in a TOF camera under various SNR conditions. Experimental verification confirms the validity of the model using real data for range error classification under different noise conditions.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55340
Title: Influence of sample size on line-based positional assessment methods for road data
Author: F J Ariza-Lopez, A T Mozas-Calvache, M A Urena-Camara, V Alba-Fernandez, J L Garcia-Balboa, J Rodriguez-Avi, J J Ruiz-Lendinez
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Accuracy, quality, sampling, simulation, spatial
Abstract: Sample size influence considerably the variability of results of estimation processes, but this issue has never previously been analyzed for line-based positional assessment methods. The basis of the analysis is a simulation process which extracts homologous road axes from the product and from the control survey and applies the four methods. Sample sizes of road axes range from 10 km upto 200 km and for each sample size 1000 simulations were run. Results for each sample size were compared to population parameters or population distribution functions by means of statistical tests. The variability of estimations was reduced in the order of 2.5-4.5 times when sample size increased from 10 km to 200 km.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55339
Title: Relative orientation based on multi-features
Author: Yongjun Zhang, Binghua Hu, Jianging Zhang
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Mutli-features, straight line, circular curve, relative orientation, precision anlaysis
Abstract: In digital photogrammetry, corresponding points have been widely used as the basic source of information to determine the relative orientation parameters among adjacent images. Sometimes, though, the conventional relative orientation process cannot be precisely implemented due to the accumulation of random errors or in the case of inadequate corresponding points. A new relative orientation approach with multiple types of corresponding features, including points, straight lines, and circular curves, is proposed in this paper. The origin of the model coordinate system is set at the projection center of the first image of a strip, and all of the exterior orientation parameters, except ? and w of the first image, are set at zero. The basic models of relative orientation with corresponding points, straight lines, and circular curves are discussed, and the general form of a least squares adjustment model for relative orientation based on multi-features is established. Our experimental results show that the proposed approach is feasible and can achieve more reliable relative orientation results than the conventional approach based on corresponding points only.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55338
Title: DEM matching for bias compensation of rigorous pushbroom sensor models
Author: Taejung Kim, Jaehoon Jeong
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: DEM matching, pushbroom image, sensor modelling, automation, SPOT
Abstract: DEM matching is a technique to match two surfaces or two DEMs, at different reference frames. It was originally proposed to replace the need of ground control points for absolute orientation of perspective images. This paper examines DEM matching for precise mapping of pushbroom images without ground control points. We proved that DEM matching based on 3D similarly transformation can be used when model errors are only on the platform ' s position and attitude biases. We also proposed how to estimate bias errors and how to update rigorous pushbrrom sensor models from DEM matching results. We used a SPOT-4 stereo pair at ground sampling distance of 2.5 m and a reference DEM dataset at grid spacing of 30 m and showed that rigoroud pushbroom models with accuracy better than twice of the ground sampling distance both in image and object space have been achieved through DEM matching. We showed further that DEM matching based on 3D similarly transformation may not work for pushbroom images with drift or drift rate errors. We discussed the effects of DEM outliers on DEM matching and automated removal of outliers. The major contribution of this paper is that we validate DEM matching, theoretically and experimentally, for estimating position and attitude biases and for establishing rigorous sensor models for pushbroom images.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55337
Title: Use of field reflectance data for crop mapping using airborne hyperspectral image
Author: Rama Rao Nidamanuri, Bernd Zbell
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Field spectrometry, HyMAP, Hyperspectral remote sensing, Crop classification, Spectral library
Abstract: Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e., alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture turned matched filtering (MTMF), (2) spectral feature fitting (SFF) and (3) spectral angle mapper (SAM) methods. In order to answer a general research question "what is the prospect of using independent reference reflectance spectra for image classification", while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barely, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of "non-existence of characteristic reflectance spectral signatures for vegetation", results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55336
Title: Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance
Author: Shalei Song, Wei Gong, Bo Zhu, Xin Huang
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Hyperspectral data, Wavelength selection, spectral discrimination, rice
Abstract: The objective of this research is to select the most sensitive wavelengths for the discrimination of the imperceptible spectral variations of paddy rice under different cultivation conditions. The paddy rice was cultivated under four different nitrogen cultivation levels and three water irrigation levels. There are 2151 hyperspectral wavelengths available, both in hyperspectral reflectance and energy space transformed spectral data. Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf bio-chemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses. Moreover, several significant narrow bands and other broad bands were selected to establish eight kinds of wavelength (broad-band) combinations, focussing on comparing the performance of the narrow-band combinations instead of broad-band combinations for rice supervising applications. Finally, to investigate the capability of the selected wavelengths to diagnose the stress conditions across the different cultivation levels, four selected narrow bands (552, 675, 705 and 776 nm) were calculated and compared between nitrogen-stressed and non-stressed rice leaves using linear discriminant analysis (LDA), Also, wavelengths of 1158, 1378 and 1965 nm were identified as the most useful bands to diagnose the stress condition across three irrigation levels. Results indicated that good discrimination was achieved. Overall, the narrow bands based on hyperspectral reflectance data appear to have great potential for discriminating rice of differing cultivation conditions and for detecting stress in rice vegetation; these selected wavelengths also have great potential use for the designing of future sensors.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55335
Title: Use of ETM+ images to extend stem volume estimates obtained from LiDAR data
Author: Fabio Maselli, Marta Chiesi, Alessandro Montaghi, Enzo Pranzini
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Stem volume, LiDAR, Landsat ETM+, k-NN, Local regression
Abstract: Airborne LiDAR techniques can provide accurate measurements of tree height, from which estimates of stem volume and forest woody biomass can be obtained. These techniques, however, are still expensive to apply repeatedly over large areas. The current paper presents a methodology which first transforms mean stand heights obtained from LiDAR over small strips into relevant stem volume estimates. These are then extended over an entire forest by applying two estimation methods (k-NN and locally calibrated regression) to Landsat ETM+ images. The methodology is tested over a coastal area covered by pine forest in the Regional Park of San Rossore (Central Italy). The results are evaluated by comparison with the ground stem volumes of a recent forest inventory, taking into consideration the effect of stand size. In general, the accuracies of two estimation methods are dependent on the size of the forest stands and are satisfactory only when considering stands larger than 5-10 ha. The outputs of the parametric regression procedure are slightly more stable than those of k-NN and more faithfully reproduce the spatial patterns of the ground data.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55334
Title: Identification of hazelnut fields using spectral and Gabor textural features
Author: Selcuk Reis, Kadim Tasdemir
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Orchard detection, Hazel orchards, Texture analysis, Multi-scale Gabor features, Self-organizing maps, maximum likelihood classifier
Abstract: Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchars are planted irregularly and densly at fields with high slope, textural delineation of these orchards from other woody vegetaton becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F? values ( a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F? values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55333
Title: Prediction of L-band signal attenuation in forests using 3D vegetation structure from airborne LiDAR
Author: Pang-Wei Liu, Heezin Lee, Jasmeet Judge, William C Wright, K Clint Slatton
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Airborne LiDAR, Microwave attenuation, Remote sensing, GPS, 3D vegetation structure
Abstract: In this study, we propose a novel method to predict microwave attenuation in forested areas by using airborne Light Detection and Ranging (LiDAR). While propagation through a vegetative medium, micro wave signals suffer from reflection, absorption, and scattering within vegetation, which cause signal attenuation and, consequently, deterirate signal reception and information interpretation. A Fresnel zone enveloping the radio frequency line-of-sight is applied to segment vegetation structure occluding signal propagation. Return parameters and the spatial distribution of vegetation from the airborne LiDAR inside Fresnel zones are used to weight the laser points to estimate directional vegetation structure. A Directional Vegetation Density (DVD) model is developed through regression that links the vegetation structure to the signal attenuation at the L-band using GPS observations in a mixed forest in North Central Florida. The DVD model is compared with currently-used empirical models and obtained better R2 values of 0.54 than the slab-based models. Finally, the model is evaluated by comparing with GPS observations of signal attenuation. An overall root mean square error of 3.51 dB and a maximum absolute error of 9.38 dB are found. Sophisticated classification algorithms and full-waveform LiDAR systems may siginficantly improve the estimation of signal attenuation.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55332
Title: Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification
Author: Juha Suomalainen, Teemu Hakala, Harri Kaartinen, Esa Raikkonen, Sanna Kaasalainen
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Hyperspectral, Supercontinuum, LiDAR, Point cloud classification, spectral correlation mapper
Abstract: In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The mesurement system consistes of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technolgy, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using a experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task uisng only traditionl TLS data.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55331
Title: De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering
Author: Roshan Pande-Chhetri, Amr Abd-Elrahman
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: De-striping, wavelet decomposition analysis, fourier transform, hyperspectral , de-noising
Abstract: Hyperspectral imagers are built line-by-line similar to images acquired by pushbroom sensors. They can experience striping artifacts due to variations in detector response to incident imagery. In this research, a method for hyperspectral image de-striping based on wavelet analysis and adaptive Fourier zero-frequency amplitude normalization has been developed. The algorithm was tested against three other de-striping algorithms. Hyperspectral image bands of different scenes with significant striping and random noise, as well as an image with simulated noise, were used in the testing. The results were assessed visually and quantitatively using frequency domain Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and /or Peak Signal-to-Ratio (PSNR). The results demonstrated the superiority of our proposed algorithm in de-striping hyperspectral images without introducing unwanted artifacts, yet preserving image details. In the noise-induced image results, the proposed method reduced RMSE error and improved PSNR by 3.5 dB which is better than other tested methods. A Combined method, integrating the proposed algorithm with a generic wavelet-based de-noising algorithm, showed significant random noise suppression in addition to stripe reduction with a PSNR value of 4.3 dB. These findings make the algorithm a candidate for practical implementation on remote sensing images including high resolution hyperspectral images contaminated with stripe and random noise.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55330
Title: Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery
Author: Hossein Saadat, Jan Adamowski, Robert Bonnell, Forood Sharifi, Mohammad Namdar, Sasan Ale-Ebrahim
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Land use and land cover (LULC), classification, unsupervised classification, supervised classification, Normalized Difference Vegetation Index (NDVI), Golestan Dam watershed
Abstract: Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information nad analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping dpends on more than just analysis of reflectance values. In this research, it was found that incorporation climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55329
Title: Forest parameter estimation in the Pol-InSAR context employing the multiplicative- additive speckle noise model
Author: Carlos Lopez-Martinez, Xavier Fabregas, Luca pipia
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Synthetic aperture radar, Polarimeteric interferometry, Polarimetry, Coherence, Speckle,
Abstract: This paper addresses the problem of speckle noise on single baseline polarimeteric SAR interferometry (Pol-InSAR) on the basis of the multiplicative-additive speckle noise model. Considering this speckle noise model, a novel filtering technique is defined and studied in terms of simulated and experimental Pol-InSAR data. As demonstrated, the use of the multiplicative-additive speckle noise model does not lead to corruption of the useful information but to an improvement of its estimation. The performance of the algorithm is analyzed in terms of the physical parameters retrieved from the filtered data, that in this work correspond to the forest height and the ground phase. In case of experimental data, the retrieval forest height is compared and validated agaisnt Lidar ground truth measurements.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55328
Title: A cloud mask methodolgy for high resolution remote sensing data combining information from high and medium resolution optical sensors
Author: Fernando Sedano, Pieter Kempeneers, Peter Strobl, Jan Kucera, Peter Vogt, Lucia Seebach, Jesus San-Miguel-Ayanz
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, Issue 5, September 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Cloud mask, Data fusion High resolution, Medium resolution, Region growing
Abstract: This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4 -HRVIR, SPOT5-HRG and IRS-LISS III as high resolution imges and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agricultural land, built-up areas. water bodies and snow.
Location: 241
Literature cited 1: None
Literature cited 2: None