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Home PE&RS Journals In Press Peer Reviewed Articles

PE&RS Journals

In Press Peer Reviewed 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.

August 2015 Issue

Accurate Affine Invariant Image Matching Using Oriented Least Square

Amin Sedaghat and Hamid Ebadi

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Image matching is a vital process for many photogrammetric and remote sensing applications such as image registration and aerial triangulation. In this paper, an accurate affine invariant image matching approach is presented. The proposed approach consists of three main steps. In the first step, two affine invariant feature detectors, including MSER and Harris-Affine features are applied for feature extraction. In the second step, initial corresponding features are selected using Euclidean distance between feature descriptors, followed by a consistency check process. Finally to overcome low positional accuracy of the local affine feature, an advanced version of the least square matching (LSM) namely, Oriented Least Square Matching (OLSM) is developed. Well-known LSM method has been widely accepted as one of the most accurate methods to obtain high reliable corresponding points from a stereo image pair. However, it is sensitive to significant geometric distortion and requires very good initial approximation. In the proposed OLSM method, shape and size of the matching window are appropriately approximated using obtained affine shape information of the initial elliptical feature pairs. The proposed method was successfully applied for matching various synthetic and real close range and satellite images. Results demonstrate its accuracy and capability compared to standard LSM method.


Filtering Global Land and Surface Altimetry Data (GLA14) for Elevation Accuracy Determination

Jean-Samuel Proulx-Bourque, Ramata Magagi, and Norman T. O'Neill

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This paper presents a filtering method for ICESat Global Land Surface Altimetry data (GLA14), which is based on indicators to detect potentially contaminated GLA14 elevation points. Potential contamination sources include attitude miscalculation, saturated echoes, equipment noise, the atmosphere, and variable elevation within footprints. For a study site located in Northern Canada, this multi-indicator filter provided a 19 percent reduction in the root mean square error for elevation, when compared to Canadian Digital Elevation Data (CDED). This result demonstrates the method's ability to provide an improved dataset for vertical accuracy evaluation, with respect to unfiltered GLA14 data. The improvement was achieved with a rejection rate of 69 percent. However, due to the high density of the unfiltered GLA14 data over the study site, a spatially homogeneous distribution of elevation points was maintained, even after filtering. Results also showed the rejection efficiency of most indicators, as well as their complementarity..


Detecting Decadal Land Cover Changes in Mining Regions based on Satellite Remotely Sensed Imagery: A Case Study of the Stone Mining Area in Luoyuan County, SE China

Zhaoming Zhang, Guojin He, Mengmeng Wang, Zhihua Wang, Tengfei Long, and Yan Peng

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Mining regions often undergo abrupt and extensive land cover changes that pose serious environmental and social impacts. In this study, decadal land cover changes in stone mining areas of Luoyuan County, southeastern China from 2001 to 2010 were examined based on multi-source satellite remote sensing imageries including ALOS, SPOT2, and Landsat-7. Object-oriented classifications combined with decision tree and retrospective approaches are employed to extract land cover and change information for the ten-year period. The study results show that the stone mining area nearly quadrupled over the ten-year period. It is found that the digging area accounts for only 14.3 percent of the stone mining region. However, mine dumps and tailings occupy the majority of the region, a remarkable characteristic distinct from other mining regions. Reclaimed land in the mined region is very limited. An evident increase in the extent of urbanized land cover is also observed in the study area for the last decade.


Extracting Pavement Surface Distress Conditions Based on High Spatial Resolution Multispectral Digital Aerial Photography

Su Zhang, Susan M. Bogus, Christopher D. Lippitt, Paul R.H. Neville, Guohui Zhang, Cong Chen, and Vanessa Valentin

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State transportation agencies regularly collect data on pavement surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair decisions. Routinely-acquired and publically-available high spatial resolution (HSR) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regression models were used to evaluate the potential of using HSR multispectral, digital aerial photographs to estimate pavement surface overall distress conditions. Various models were developed using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R2 > 0.95), even when using only half of the sample sites (R2 > 0.92). These results indicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.


Removal of Thin Clouds Using Cirrus and QA Bands of Landsat-8

Yang Shen, Yong Wang, Haitao Lv, and Hong Li

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After atmospheric correction, an algorithm for the removal of thin cirrus cloud as well as alto-thin clouds or thin clouds collectively within visible and near infrared bands (Bands 1 through 5) of Landsat-8 was developed. The algorithm removed cirrus clouds using Band 9 first, and the remaining thin clouds using quality assurance (QA) band. Using a Landsat-8 sub-image of 129/39 (path/row) acquired on 16 December 2013, we evaluated the algorithm. Thin clouds disappeared visually. Reflectance values of Bands 1 through 4 decreased in both steps. Reflectance values of Band 5 decreased in step one, and then stayed the same. With a nearly cloud-free image acquired on 30 November 2013 as the "truth," the spatial correlation coefficients of cloud-covered pixels within the December image were 0.84 or higher. Changes in reflectance values of Bands 1 to 5, and the high correlation coefficient values indicated the validity of the algorithm. 


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