On the verification and validation of geospatial image analysis algorithms

Verification and validation (V&V) of geospatial image analysis algorithms is a difficult task and is becoming increasingly important. While there are many types of image analysis algorithms, we focus on developing V&V methodologies for algorithms designed to provide textual descriptions of geospatial imagery. In this paper, we present a novel methodological basis for V&V that employs a domain-specific ontology, which provides a naming convention for a domain-bounded set of objects and a set of named relationships between these objects. We describe a validation process that proceeds through objectively comparing benchmark imagery, produced using the ontology, with algorithm results. As an example, we describe how the proposed V&V methodology would be applied to algorithms designed to provide textual descriptions of facilities.

[1]  Marinos Kavouras,et al.  Theories of Geographic Concepts: Ontological Approaches to Semantic Integration , 2007 .

[2]  Werner Ceusters,et al.  Ontology and medical terminology: Why description logics are not enough , 2003 .

[3]  Veronica Carlan,et al.  Overhead imagery research data set — an annotated data library & tools to aid in the development of computer vision algorithms , 2009, 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009).

[4]  Horst Bunke,et al.  Distance Measures for Image Segmentation Evaluation , 2006, EURASIP J. Adv. Signal Process..

[5]  Leo Obrst,et al.  The Evaluation of Ontologies: Toward Improved Semantic Interoperability , 2006 .

[6]  Liang Lin,et al.  I2T: Image Parsing to Text Description , 2010, Proceedings of the IEEE.

[7]  Michael Dominguez Navy Modeling and Simulation Management Office , 2002 .

[8]  William L. Oberkampf,et al.  Guide for the verification and validation of computational fluid dynamics simulations , 1998 .

[9]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[10]  Kei-Hoi Cheung,et al.  Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences , 2006 .

[11]  B. Hammond Ontology , 2004, Lawrence Booth’s Book of Visions.

[12]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[13]  Leonard E. Schwer,et al.  An overview of the PTC 60/V&V 10: guide for verification and validation in computational solid mechanics , 2007, Engineering with Computers.

[14]  Cordelia Schmid,et al.  Dataset Issues in Object Recognition , 2006, Toward Category-Level Object Recognition.

[15]  Yue Jiang,et al.  Verification and Validation of a Fingerprint Image Registration Software , 2006, EURASIP J. Adv. Signal Process..

[16]  R Klein,et al.  ASC Predictive Science Academic Alliance Program Verification and Validation Whitepaper , 2006 .

[17]  IEEE Guide for Software VeriÞcation and Validation Plans , 2000 .

[18]  G. Hay,et al.  Special Issue on Geographic Object-Based Image Analysis (GEOBIA). , 2010 .

[19]  Nicolas Pinto,et al.  Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..