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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.


February 2016 Issue

Seamline Determination for High Resolution Orthoimage Mosaicking Using Watershed Segmentation

Mi Wang, Shenggu Yuan, Jun Pan, Liuyang Fang, Qinghua Zhou, and Guopeng Yang

Abstract Download Full Article (members only)

Image mosaicking is a process during which multiple orthoimages are combined into a single seamless composite orthoimage. One of the most difficult steps in the automatic mosaicking of orthoimages is the seamline determination. This paper presents a novel algorithm that selects seamlines based on marker-based watershed segmentation. A representative seamline is extracted at the object level and the pixel level as follows. First, a watershed segmentation is performed to obtain the objects. To avoid over-segmentation, a regional adaptive marker-based watershed segmentation is proposed. Second, the object difference estimated by the correlation coefficient of each object is calculated, and the region adjacency matrix is built. Third, a technique for minimizing the maximum object cost is adopted to determine the objects through which the seamlines pass. Finally, pixel-level optimization is performed using Dijkstra’s algorithm with a binary min-heap to determine the final seamlines. The experimental results on digital aerial orthoimages in different areas demonstrate the feasibility and effectiveness of the proposed method compared with other algorithms.


Discriminating Spectral Signatures Among and Within Two Closely Related Grapevine Species

Matthew Maimaitiyiming, Allison J. Miller, and Abduwasit Ghulam

Abstract Download Full Article (members only)

Several North American Vitis species are used to breed scions and rootstocks, including V. riparia and V. rupestris. However, the degree to which Vitis species can be distinguished using remote sensing is not well known. Here we explore whether two North American Vitis species and genotypes growing in a common garden can be discriminated with leaf and canopy hyperspectral reflectance factor data (350-2500 nm) using independent t-test and derivative analysis. Foliar properties and spectral indices of the grapevines were evaluated with analysis of the variance (ANOVA) and pair-wise Bonferroni adjusted t-tests. The results showed that V. riparia and V. rupestris can be distinguished at the leaf level spectra of visible, near- and infrared spectral regions. At the canopy level, genotypes were spectrally discriminated with limited success. The Photochemical Reflectance Index (PRI) demonstrated the highest potential not only to differentiate two species, but also two genotype pair groups within V. rupestris. This finding was also true for the PRI calculated with simulated EO-1 Hyperion data. These capacities to distinguish Vitis species, and to a lesser extent genotypes, using spectral signatures have important applications in remote monitoring of vineyards for plants health and also for locating wild Vitis populations for future crop improvement efforts.


 Multi-Criteria, Graph-Based Road Centerline Vectorization Using Ordered Weighted Averaging Operators

 Fateme Ameri, Mohammad Javad Valadan Zoej, and Mehdi Mokhtarzade

Abstract Download Full Article (members only)

In this paper a novel road vectorization methodology based on image space clustering technique and weighted graph theory is presented. The proposed methodology describes a road as a set of optimized points on the centerline which should be connected by defining a number of appropriate criteria. The main contribution of this paper is to design a weighting scheme for combining a small number of road identities using Ordered Weighted Averaging (OWA) operators by defining appropriate decision strategy. In this regard, a novel geometric criterion is introduced. Result of the OWA aggregation specifies weight of each edge in the road network graph. Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters. Obtaining improved quality measures for several high-resolution images, demonstrates the successfulness of the vectorization approach.


 Sliver Removal in Object-Based Change Detection from VHR Satellite Images

 Luigi Barazzetti

Abstract Download Full Article (members only)

This paper presents a novel strategy for object-based change detection using very high spatial resolution (VHR) satellite images captured under variable off-nadir view angles. The variable off-nadir angle, along with weak absolute orientation, generates spurious slivers during the multitemporal comparison of classification results. The proposed solution for accurate object-to-object comparison is based on an intermediate registration of object-based classification results with a piecewise affine transformation followed by robust, geometry-based techniques for sliver removal. Although different remote sensing applications require different strategies and methods for object-based change detection, the approach developed in this paper can overcome the overall limitation introduced by the slivers generated by weak geo-localization, variable off-nadir angles, and image segmentation. 


 A Region-Line Primitive Association Framework for Object-Based Remote Sensing Image Analysis

 Min Wang and Jie Wang

Abstract Download Full Article (members only)

In this study, we propose a novel region-line primitive association framework (RLPAF) for OBIA. In this framework, segments (region primitive) and straight lines (line primitive) are obtained by image segmentation and straight line detection, respectively, before their corresponding intra-primitive features are extracted. An association model is built on inter-primitive topology and direction relationships. Several region-line collaborative features are also derived. Image analysis is then performed based on both region and line primitives. The advantage of RLPAF is the collaborative utilization of complementary information between regions and lines throughout the entire OBIA process: from image segmentation, to feature extraction, and finally, object recognition. To validate this framework, RLPAF is applied on road network extraction from high spatial resolution (HSR) remote sensing images. Experiments show that the proposed framework and methods refine primitive shape and spatial relationship analyses, as well as obtain higher method accuracy, than OBIAs based on only regions.


SRTM Error Distribution and its Associations with Landscapes across China

Quan Zhang, Qinke Yang, and Chunmei Wang

Abstract Download Full Article (members only)

In this paper the distribution of 3-second elevation error in the data from the Shuttle Radar Topography Mission (SRTM) over the whole of China and its associations with topographic and land cover factors were systematically evaluated. The landscape features extracted from different datasets at more than 500,000 sites were used to determine the variation pattern in the errors by the method of single factor analysis. The results showed that the topographic attributes derived from SRTM data could adequately represent the terrain of China. However, there were extended and observable areas with abnormalities in a small proportion of the data. Slope was the dominant factor affecting elevation error compared with other landscape features (aspect, vegetation, etc.). The mean errors in glaciers, deserts and wetlands were -1.05 m, -2.03 m and -2.43 m, and 1.05 m in built-up areas. In general the elevation errors in the SRTM data formed a complex pattern of variation across China.

 

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