Photogrammetric Engineering & Remote Sensing In-Press Articles (Members Only)
In Press Articles
As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.
Historic Calibration of the
Thermal Infrared Band of Landsat-5 TM
Francis P. Padula and John R. Schott
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This investigation was motivated by the need for radiometric calibration of the Landsat-5 Thematic Mapper (TM) thermal band (Band 6), covering its 25 years of operation. Through fusion of environmental data and physical modeling, a vicarious calibration approach was implemented. Results, which were demonstrated to agree with data from previous field campaigns, indicate the Landsat-5 thermal band has been calibrated to better than 1 K over the 24- years in orbit. The data indicate the sensor exhibits a small gain error (approximately constant over time) and a bias shift for all data post-1999. The lifetime calibration error can be reduced to 0.49 K, in reference to a 300 K blackbody, by a pair of linear corrections. This study established that the methodology presented can be implemented to achieve a very high level of radiometric integrity, applicable to both historic and current data from remote thermal infrared systems.
Detection Thresholds for Rare, Spectrally
Unique Targets within Semiarid Rangelands
Keith T. Weber and Fang Chen
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Many factors influence classification accuracy, and this study assessed detection thresholds for various sub-pixel targets using QuickBird multispectral imagery. Six iterations of maximum-likelihood classification were used to determine classification accuracy for 100 spectrally unique targets randomly placed over a semiarid rangeland site. Error matrices were calculated using independent validation sites and producer’s, user’s, and overall accuracy, Kappa Index of Agreement, and transformed divergence were analyzed to compare the performance of each classification and determine detection thresholds. Results indicate a strong relationship between target size and classification accuracy (R2 - 0.94) as well as an increasingly prominent role played by training site selection as target size decreased. Strong spectral separability and good classification accuracies were achieved for targets 25 percent cover. Sub-pixel targets 25 percent in size were not detectable. This study highlights the effect of target size upon classification accuracy and has direct implications for invasive plant research and rare target detection.
Improved Subspace Classification Method for
Multispectral Remote Sensing Image
Classification
Hasi Bagan and Yoshiki Yamagata
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This paper proposes a modified subspace classification method (MSCM) that integrates an extended multiple similarity method with the averaged learning subspace method to achieve improved land-cover classification performance. Furthermore, we developed an automatic parameter selection and optimization technique for MSCM to avoid time-consuming, laborious manual parameter tuning. Only three parameters need to be set, and their optimal values are easily determined by the automatic procedure. We carried out experiments with data of two multispectral images: Landsat TM data for a semi-arid area in the Horqin sandy land, China, and ASTER data for the Kasumigaura Lake region, Japan, a high humidity, warm temperature zone. Accuracy assessment of the MSCM results in comparison with those of the support vector machine (SVM) and maximum likelihood classification (MLC) methods showed that the MSCM yielded better classification results. Therefore, the proposed MSCM shows promise as a tool for land-cover classification.
Reflectance Spectroscopy of Biochemical
Components as Indicators of Tea (Camellia
Sinensis) Quality
Meng Bian, Andrew K. Skidmore, Martin Schlerf, Teng Fei, Yanfang Liu, and Tiejun Wang
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The potential of reflectance spectroscopy to estimate the concentration of biochemical compounds related to tea (Camellia sinensis (L.)) quality (total tea polyphenols and free amino acids) is demonstrated. Partial least squares regression (PLSR) was performed to establish the relationship between reflectance and biochemicals for leaf powders as well as fresh leaves. Highest accuracy was found for tea powders with a cross-validated r2 of 0.97 for tea polyphenols and 0.99 for free amino acids, and the root mean square error of cross validations (RMSECVS) are 8.36 mg g-1 and 1.01 mg g-1 for the two chemicals. The accuracy achieved at leaf level was slightly lower, with results yielding cross-validated r2 of 0.91 and 0.93 with RMSECVS of 13.74 mg g-1 and 2.32 mg g-1 for tea polyphenols and free amino acids, respectively. Important wavelengths for prediction of the two biochemicals from powder and leaf spectra were identified using the PLSR bcoefficients as indicators. Wavelengths of 1,131 nm, 1,654 nm, 1,666 nm, 1,738 nm and 1,752 nm were identified as bands related to absorption by total tea polyphenols, while 1,492 nm represented the absorption feature of free amino acids. The results obtained using fresh leaves indicate that hyperspectral remote sensing may be useful for routine monitoring of tea chemistry at landscape scale.
Off-nadir Viewing for Reducing Spectral
Mixture Issues in Citrus Orchards
Jan Stuckens, Ben Somers, Gene L. Albrigo, Sebinasi Dzikiti, Willem W. Verstraeten,
Rony Swennen, Stephan Verreyne and Pol Coppin
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A modeling approach to determine optimal viewing angles that reduce the impact of signal mixtures in orchards was evaluated on citrus. Orchards statistics from high-resolution images in Florida and Western Cape province (South Africa) citrus producing areas show that pixel unmixing between adjacent fields can be avoided for pixel sizes below 10 to 15 m, while within fields mixing (canopy, soil, and weeds) is inevitable with multi- and hyperspectral sensors. A virtual 3D environment was constructed in which offnadir pointing simulations were made for different field orientations. Spectral similarity based contribution indices were derived to find overflight times and viewing angles that reduce the impact of seasonality, soil and weeds. Although no single optimal view direction was found for all scenarios, optimal results in mature orchards were consistently obtained by early acquisitions and low oblique viewing perpendicular to or at a 45° angle with the row orientation, opposite to the solar azimuth.
Positional Accuracy Analysis of Satellite
Imagery by Circular Statistics
A. Cuartero, A.M. Felicísimo, M.E. Polo, A. Caro, and P.G. Rodríguez
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The proposed method in this paper uses circular statistics for the analysis of errors in the positional accuracy of geometric corrections satellite images using Independent Check Lines (ICL) instead of Independent Check Points (ICP). Circular statistics has been preferred because of the vectorial nature of the spatial error. A study case has been presented and discussed in detail. From the TERRA-ASTER images of Extremadura area (Spain), the Ground Control Point (GCP), ICP, and ICL data were acquired using differential GPS through field survey, and the planimetric positional accuracy was analyzed by both the conventional method (using ICP) and the proposed method (using ICL). Comparing conventional and proposed methods, the results indicated that modulus statistics are similar (e.g., RMSE of Geometric Correction 1 were 17.5 for the conventional method and 17.2 m for proposed method). But as additional results, azimuthal component statistics was calculated (e.g., mean direction: 247.2°in Geometric Correction 1), and several tests were made which showed the error distribution are not uniform and normal.
3D SAR Simulation of Urban Areas
Based on Detailed Building Models
Stefan Auer, Timo Balz, Susanne Becker, and Richard Bamler
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This paper confirms the need for detailed 3D models for the simulation of high-resolution SAR images in order to support Persistent Scatterer Interferometry (PSI) focused on single urban objects. Using a building model enhanced by facade grammar, multiple reflections at building facades are analyzed using ray tracing techniques and scatterers are localized in azimuth, range, and elevation. In a case study, salient signatures of a TerraSAR-X image are analyzed based on simulated SAR reflectivity maps. Phase centers of trihedral reflections are mapped onto the building model and the physical correspondence of scatterers to building features is investigated. Surfaces contributing to salient scatterers are identified at the building model. Eventually, the use of SAR simulation to support PSI is shown from two aspects: (a) for providing a-priori information about building layover, and (b) for extending knowledge about the nature of dominant scatterers.
3D Building Model Reconstruction from
Multi-view Aerial Imagery and Lidar Data
Liang Cheng, Jianya Gong, Manchun Li, and Yongxue Liu
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A novel approach by integrating multi-view aerial imagery and lidar data is proposed to reconstruct 3D building models with accurate geometric position and fine details. First, a new algorithm is introduced for determination of principal orientations of a building, thus benefiting to improve the correctness and robustness of boundary segment extraction in aerial imagery. A new dynamic selection strategy based on lidar point density analysis and K-means clustering is then proposed to identify boundary segments from non-boundary segments. Second, 3D boundary segments are determined by incorporating lidar data and the 2D segments extracted from multi-view imagery. Finally, a new strategy for 3D building model reconstruction including automatic recovery of lost boundaries and robust reconstruction of rooftop patches is introduced. The experimental results indicate that the proposed approach can provide high quality 3D models with high-correctness, high-completeness, and good geometric accuracy.
High Resolution Impervious Surface
Estimation: An Integration of Ikonos and
Landsat-7 ETM+ Imagery
Rama Prasada Mohapatra and Changshan Wu
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Recent studies have attempted to extract impervious surfaces from high-resolution satellite imagery such as Ikonos and QuickBird. These images, however, often lack necessary spectral information due to technological limitations. This study integrates spectral information (temperature and moisture) derived from Landsat-7 ETM+ imagery with Ikonos imagery to derive high-resolution impervious surface information. Furthermore, three popular methods, including linear regression modeling, artificial neural network, and regression tree have been developed and compared using a paired t-test statistic. Analysis of results reveal that Tasseled Cap components particularly greenness and wetness of Ikonos imagery are most important in estimating sub-pixel imperviousness. Also, to some extent the brightness temperature derived from Landsat-7 ETM+ image helps in better estimation of impervious surfaces. Moreover, a comparative analysis indicates that the non-linear approaches yielded statistically better results. Particularly, the regression tree model generated best results with highest Pearson’s r (0.939) and lowest mean absolute error (8.307).
Migration Patterns of Two Endangered
Sympatric Species from a Remote
Sensing Perspective
Tiejun Wang, Andrew K. Skidmore, Zhigao Zeng, Pieter S.A. Beck,
Yali Si, Yanling Song, Xuehua Liu, and Herbert H.T. Prins
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Giant pandas (Ailuropoda melanoleuca) and golden takin (Budorcas taxicolor bedfordi) are large mammals that occur together throughout the southern part of the Qinling Mountains in China. Both species have the habit of altitudinal migration in a mixed forest-bamboo landscape. Although previous studies have reported that the migration patterns of giant pandas and golden takin seem different, little is known about these differences in relation to their food quality and quantity. We used radio-telemetry data from six giant pandas and three golden takin groups to determine whether differences in their migration patterns are related to satellitederived plant phenology (a surrogate of food quality) and bamboo abundance (a surrogate of food quantity). Our results suggest that the altitudinal migration patterns of both the giant panda and the golden takin follow the phenological development of plants in the study area, and the difference between them seems to be attributable to the difference in the phenology of bamboo and non-bamboo plants, and thus the abundance and quality of food available to these two species.
A Piecewise Approach to Epipolar Resampling
of Pushbroom Satellite Images Based on RPC
Jaehong Oh, Won Hee Lee, Charles K. Toth, Dorota A. Grejner-Brzezinska, and Changno Lee
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Epipolar line determination and image resampling are important steps for stereo image processing. Unlike frame cameras that have well-known epipolar geometry, the pushbroom camera does not produce straight epipolar lines and the epipolar pair does not exist for the entire scene. These properties make it difficult to establish epipolar geometry of the pushbroom camera for epipolar image resampling. Therefore, some researchers have adopted approximate models to avoid these problems. In this study, a new method for the conjugate epipolar curve pair determination and epipolar resampling of spaceborne pushbroom images based on RPC is proposed. The proposed method assumes that the conjugate epipolar curve pairs exist approximately for the local scene area and the global epipolar pairs can also exist if the local pairs are sequentially linked. Then, epipolar image resampling is established by reassigning the generated conjugate epipolar curve pair points to satisfy the epipolar resampling image condition. Ikonos stereo images are tested for the evaluation, and the proposed method showed a maximum y-parallax of 1.25 pixels for manually measured tie points, while the resampling method by the parallel projection model showed a maximum of 4.59 pixels.
Total Variation Methods for Three
Dimensional Lidar Image Denoising
Frank Crosby, Haomin Zhou, and Quyen Huynh
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New imaging capabilities have given rise to higher dimensional image processing. This paper presents a generalization of total variation (TV) based denoising model with a specific application to three-dimensional flash lidar imagery. The generalization uses a weighted norm, rather than the standard Euclidean measure, that accounts for sampling differences that may exist along different axes. We compare this new method against successive two-dimensional denoising and three-dimensional TV denoising. The results show that denoising with a weighted norm presents much better object recognition for multi-plane objects compared with both of the other methods. Further, it does not blur single plane objects beyond the result obtained with two-dimensional denoising.
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