PE&RS September 2009

Peer-Reviewed Articles

1059 A Fast Approach to Best Scanline Search of Airborne Linear Pushbroom Images
Mi Wang, Fen Hu, Jonathan Li, and Jun Pan

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The linear pushbroom camera has become one of the most important imaging sensors in today’s photogrammetry and remote sensing practices. Airborne digital sensors (ADS) or three-line scanner (TLS) imaging system such as the ADS40 from Leica Geosystems and STARIMAGER from STARLABO Corporation use the pushbroom technique to collect high-resolution, multi-channel seamless image strips. As we know, the object-to-image coordinate computation serves as a core step during the process of photogrammetric images. However, each scanline captured by a linear pushbroom sensor has six exterior orientation (EO) parameters at the corresponding instant of exposure. The image point coordinates will not be accurately calculated through colinearity equations unless reasonable EO parameters are determined. Therefore, the best scanline search (BSS) has direct effects on efficiency of object-to-image coordinate computation during image processing. This paper addresses a fast BSS method based on the novel central perspective plane of scanline (CPPS) constraints. The search process is simply performed through analytical geometric calculations, which can significantly improve the efficiency of the object-to-image coordinate computation. The feasibility and robustness of the proposed method have been verified using ADS40 and STARIMAGER images. Experimental results show that the proposed method improves scanline search speed considerably with the time cost decreased by nearly 85 percent compared with the traditional methods.

1069 Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery
Kasper Johansen, Stuart Phinn, Christian Witte, Seonaid Philip, and Lisa Newton

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The objectives of this research were to develop and evaluate an approach for object-oriented mapping of banana plantations from SPOT-5 imagery, and to compare these results to banana plantations manually delineated from high spatial resolution airborne imagery. Cultivated areas were first identified through large spatial scale mapping using spectral and elevation data. Within the cultivated areas, separation of banana plantations and other land-cover classes increased when including image co-occurrence texture measures and context relationships in addition to spectral information. The results showed that a pixel size of ≤2.5 m was required to accurately identify the row structure within banana plantations, which enabled object-based separation from other crops based on texture information. The user’s and producer’s accuracies for mapping banana plantations increased from 73 percent and 77 percent, respectively, to 94 percent and 93 percent after post-classification visual editing. The results indicate that the data and processing techniques used offer a reliable approach for mapping banana plants and other plantation crops.

Color Figures (Adobe PDF format):

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1083 A Generic Method for RPC Refinement Using Ground Control Information
Zhen Xiong and Yun Zhang

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Geometric sensor models are used in image processing to model the relationship between object space and image space and to transform image data to conform to a map projection. An Rational Polynomial Coefficient (RCP) is a generic sensor model that can be used to transform images from a variety of different high resolution satellite and airborne remote sensing systems. To date, numerous researchers have published papers about RPC refinement, aimed at improving the accuracy of the results. So far, the Bias Compensation method is the one that is the most accepted and widely used, but this method has rigorous conditions that limit its use; namely, it can only be used to improve the RPC of images obtained from cameras with a narrow field of view and small attitude errors, such as those used on Ikonos or QuickBird satellites. In many cases, these rigorous conditions may not be satisfied (e.g., cameras with a wide field of view and some satellites with large ephemeris and attitude errors). Therefore, a more robust method that can be used to refine the RPC under a wider range of conditions is desirable. In this paper, a generic method for RPC refinement is proposed. The method first restores the sensor’s pseudo position and attitude, then adjusts these parameters using ground control points. Finally a new RPC is generated based on the sensor’s adjusted position and attitude. We commence our paper with a review of the previous ten years of research directed toward RPC refinement, and compare the characteristics of different refinement methods that have been proposed to date. We then present a methodology for a proposed generic method for RPC refinement and describe the results of two sets of experiments that compare the proposed Generic method with the Bias Compensation method. The results confirm that the Bias Compensation method works well only when the aforementioned rigorous conditions are met. The accuracy of the RPC refined by the Bias Compensation method decreased rapidly with the sensor’s position error and attitude error.

In contrast to this, the Generic method proposed in this paper was found to yield highly accurate results under a variety of different sensor positions and attitudes.

1093 Error Budget of Lidar Systems and Quality Control of the Derived Data
Ayman Habib, Ki In Bang, Ana Paula Kersting, and Dong- Cheon Lee

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Lidar systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality assurance of lidar systems and quality control of the derived data has not followed the same trend. In other words, a lack of reliable, practical, cost-effective, and commonly-acceptable methods for quality evaluation is evident. A frequently adopted procedure for quality evaluation is the comparison between lidar data and ground control points. Besides being expensive, this approach is not accurate enough for the verification of the horizontal accuracy, which is known to be worse than the vertical accuracy. This paper is dedicated to providing an accurate, economical, and convenient quality control methodology for the evaluation of lidar data. The paper starts with a brief discussion of the lidar mathematical model, which is followed by an analysis of possible random and systematic errors and their impact on the resulting surface. Based on the discussion of error sources and their impact, a tool for evaluating the quality of the derived surface is proposed. In addition to the verification of the data quality, the proposed method can be used for evaluating the system parameters and measurements. Experimental results from simulated and real data demonstrate the feasibility of the proposed tool.

1109 The Effect of Prior Probabilities in the Maximum Likelihood Classification on Individual Classes: A Theoretical Reasoning and Empirical Testing
Zheng Mingguo, Cai Qianguo, and Qin Mingzhou

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The effect of prior probabilities in the maximum likelihood classification on individual classes receives little attention, and this is addressed in this paper. Prior probabilities are designed only for overlapping spectral signatures. Accordingly, their effect on an individual class is independent of the classes that are spectrally separable from this class. The theoretical reasoning reveals that an increased prior probability, which shifts the decision boundary away from the class mean, will increase the assignment and boost the producer’s accuracy as compared to the use of equal priors; though the change of the user’s accuracy is not constant, it is expected to decrease in most cases. The tendency is just the opposite when a lower prior probability is used. A case study was conducted using Landsat TM data provided along with ERDAS Imagine® software. Two other pieces of evidence derived from the published literature are also presented.

Color Figures (Adobe PDF format):

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1119 Impact of Imaging Geometry on 3D Geopositioning Accuracy of Stereo Ikonos Imagery
Rongxing Li, Xutong Niu, Chun Liu, Bo Wu, and Sagar Deshpande

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Special Ikonos data acquisition and investigation were
conducted to study the relationship between three-dimensional (3D) geopositioning accuracy and stereo imaging geometry, in particular, convergence angles. Six Ikonos images (four on one track and two on another track) were collected for a test site at Tampa Bay, Florida, in 2004 and 2007, respectively. Different combinations of Ikonos stereo image pairs, both along-track and cross-track, were formed. Using the highresolution satellite image processing system developed at The Ohio State University, DGPS (Differential Global Positioning System) controlled ground control points, and a number of check points, we demonstrated: (a) The convergence angle plays an important role in along-track or cross-track stereo mapping, especially in improvement of the accuracy in the vertical direction; (b) Regardless of stereo configuration (alongtrack or cross-track), the accuracy in the X (cross-track) direction is better than that in the Y (along-track) direction; and (c) Although there is a slight correlation between the convergence angle and the accuracy in the Y (along-track) direction in the case of along-track stereo configuration, no distinct relationship is found in the X (cross-track) direction. Similarly, improvement of the horizontal accuracies is found with increased convergence angles when dealing with crosstrack stereo pairs.

1127 Derivation and Validation of High-Resolution Digital Terrain Models from Mars Express HRSC-Data
Klaus Gwinner, Frank Scholten, Michael Spiegel, Ralph Schmidt, Bernd Giese, Jürgen Oberst, Christian Heipke, Ralf Jaumann, and Gerhard Neukum

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The High Resolution Stereo Camera (HRSC) onboard the Mars Express mission is the first photogrammetric stereo sensor system employed for planetary remote sensing. The derivation of high-quality digital terrain models is subject to a variety of parameters, some of which show a significant variability between and also within individual datasets. Therefore, adaptive processing techniques and the use of efficient quality parameters for controlling automated processing are considered to be key requirements for DTM generation. We present the general procedure for the derivation of HRSC high-resolution DTM, representing the core element of the systematic derivation of high-level data products by the Mars Express HRSC experiment team. We also analyze test series applying specific processing variations, including a new method for signal adaptive image preprocessing. The results are assessed based on internal quality measures and compared to external terrain data. Sub-pixel scale 3D point accuracy of better than 10 m and a DTM spatial resolution of up to 50 m can be achieved for large parts of the surface of Mars within a reasonable effort. This confirms the potentials of the applied along-track multiple stereo imaging principle and allows for a considerable improvement in our knowledge of the topography of Mars.