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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.


June 2015 Issue

Integrating User Needs on Misclassification Error Sensitivity into Image Segmentation Quality Assessment

Hugo Costa, Giles M. Foody, and Doreen S. Boyd

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Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach.

 


Multi-scale Segmentation of High-Spatial Resolution Remote Sensing Images Using Adaptively Increased Scale Parameter

Xueliang Zhang, Xuezhi Feng, and Pengfeng Xiao

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The adaptively increased scale parameter (AISP) strategy is proposed to control multi-scale segmentation based on region growing methods. AISP strategy contains a set of gradually increased scale parameters to produce nested multi-scale segments. Instead of independently assigning the set of scale parameters ahead of segmentation, the contribution of this study is to dynamically determine scale parameters during segmentation procedure, making scale parameters adaptive to specific images and cover meaningful segmentation scales. Furthermore, the effectiveness of gradually increased scale parameters on segmentation accuracy is analyzed, which gives a thorough understanding to local-oriented region growing methods. The experimental results on a set of high-resolution images proved the effectiveness of AISP on controlling multi-scale segmentation. AISP holds the application potential for object-based analysis of high-resolution images.

 


Mangrove Tree Crown Delineation from High-Resolution Imagery

Muditha K. Heenkenda, Karen E. Joyce, and Stefan W. Maier

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Mangroves are very dense, spatially heterogeneous, and have limited height variations between neighboring trees. Delineating individual tree crowns is thus very challenging. This study compared methods for isolating mangrove crowns using object based image analysis. A combination of WorldView-2 imagery, a digital surface model, a local maximum filtering technique, and a region growing approach achieved 92 percent overall accuracy in extracting tree crowns. The more traditionally used inverse watershed segmentation method showed low accuracy (35 percent), demonstrating that this method is better suited to homogeneous forests with reasonable height variations between trees. The main challenges with each of the methods tested were the limited height variation between surrounding trees and multiple upward pointing branches of trees. In summary, mangrove tree crowns can be delineated from appropriately parameterized object-based algorithms with a combination of high-resolution satellite images and a digital surface model. We recommend partitioning the imagery into homogeneous species stands for best results.

 


Object-Based Building Change Detection from a Single Multispectral Image and Pre-Existing Geospatial Information

Georgia Doxani, Konstantinos Karantzalos, and Maria Tsakiri-Strati

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Multispectral images of very high spatial resolution and vector data from geospatial databases, such as cadastral maps, are among the cost-effective and broadly available geodata in urban environments. Therefore, we aim to address building change detection based on pre-existing building footprint information and a single very high resolution multispectral image. An object-based classification methodology was developed that employs advanced scale-space filtering, unsupervised clustering, and knowledge-based classification. The developed framework effectively integrates prior vector data and multispectral observations, through incorporating the prior knowledge into the training process and defining the proper object-based classification rules. The methodology successfully identified important building changes, which were validated by employing the vector information of a building geodatabase and a QuickBird image acquired in 2003 and 2007, respectively, over urban regions in the city of Thessaloniki, Greece. The performed quantitative and qualitative evaluation indicated that the proposed analysis framework can detect the new buildings with high accuracy rates and, to a lesser degree, their exact shape and size.

 


A Fuzzy Spatial Reasoner for Multi-Scale GEOBIA Ontologies

Argyros Argyridis and Demetre P. Argialas

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In Geographic Object-Based Image Analysis (GEOBIA) an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/supervised methods, or knowledge-based methods, such as an ontology. The aim of this paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL 2) with fuzzy representations was adopted and expanded to represent fuzzy spatial relationships within the framework of GEOBIA. Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework, a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected.

 


Assessment of Wildfire Risk in Lebanon Using Geographic Object-Based Image Analysis

George Mitri, Mireille Jazi, and David McWethy

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During the past decade, Lebanon has experienced a large number of severe wildfires that have had significant social and ecological consequences. In this context, the assessment of wildfire risk is important to support planning of fire prevention measures and risk mitigation. The purpose of this study was to assess the spatial distribution of wildfire risk in Lebanon. The objectives were to identify and map (a) wildfire hazard, (b)wildfire vulnerability, and (c) wildfire risk. We developed a model using geospatial biophysical and climatic data and Geographic Object-Based Image Analysis (GEOBIA). Development of the wildfire hazard map included classification of forest fuel type, combustibility, and fire spread whereas the vulnerability map included classification of demographic vulnerability (i.e., boundary, occupation and scatter indicators) and forest vulnerability (i.e., environmental and replacement values). The resulting geospatial map of wildfire risk provided important information for potential use in fire risk management.

 


Rule Set Transferability for Object-Based Feature Extraction: An Example for Cirque Mapping

Niels S. Anders, Arie C. Seijmonsbergen, and Willem Bouten

Abstract Download Full Article (members only)

Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.

 

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