Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

Harvey, Neal R.; Theiler, James; Brumby, Steven P.; Perkins, Simon; Szymański, J.; Bloch, J. J.; Porter, Reid B.; Galassi, M.; Young, Aaron Cody

doi:10.1109/36.992801

Cited by 97 publications

(46 citation statements)

References 28 publications

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“…It has been applied in a variety of application domains, including imageprocessing problems. In the field of remote sensing, some works have already demonstrated that this evolution-based process could be useful in particular, to detect and eliminate noisy spectral bands (for hyperspectral images), and to produce comprehensible classification rules [7,8,9,10]. GP can provide relevant and robust rules in terms of classification accuracy.…”

Section: Genetic Programmingmentioning

confidence: 99%

Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data

Quirin¹,

Puissant

Gançarski³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Abstract-The classification methods applied in the objectoriented image analysis approach are often based on the use of domain knowledge. A key issue in this approach is the acquisition of this knowledge which is generally implicit and not formalized. In this paper, we examine the possibilities of using genetic programming for the automatic extraction of classification rules from urban remotely sensed data. The method proposed is composed of several steps: segmentation, feature extraction, selection of training sets, acquisition of rules, classification. Features related to the spectral, spatial and contextual properties of the objects are used in the classification procedure. Experiments are made on a Quickbird MS image. The quality of the results shows the effectiveness of the proposed genetic classifier in the object-oriented, knowledge-based approach.

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Section: Genetic Programmingmentioning

confidence: 99%

Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data

Quirin¹,

Puissant

Gançarski³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…The first is the approach used in GENIE, an evolutionary system for finding features of interest in multi-spectral remotely-sensed images [13,14]. In this system a set of morphological operators was provided as part of a larger set of image processing primitives.…”

Section: Gas For MM Filters and Algorithm Designmentioning

confidence: 99%

Morphological algorithm design for binary images using genetic programming

Quintana

Poli

Claridge

2006

Genet Program Evolvable Mach

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Abstract. This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

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“…However, only some specifi time-series domains have been tested. Similarly, [23,24] assembles image-processing primitives (edge-detectors, . .…”

Section: Related Workmentioning

confidence: 99%

GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains

2007

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When dealing with classificatio and regression problems, there is a strong need for high-quality attributes. This is a capital issue not only in financia problems, but in many Data Mining domains. Constructive Induction methods help to overcome this problem by mapping the original representation into a new one, where prediction becomes easier. In this work we present GPPE: a GP-based method that projects data from an original data space into another one where data approaches linear behavior (linear separability or linear regression). Also, GPPE is able to reduce the dimensionality of the problem by recombining related attributes and discarding irrelevant ones. We have applied GPPE to two financia domains: Bankruptcy prediction and IPO Underpricing prediction. In both cases GPPE automatically generated a new data representation that obtained competitive prediction rates and drastically reduced the dimensionality of the problem.

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Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

Cited by 97 publications

References 28 publications

Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data

Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data

Morphological algorithm design for binary images using genetic programming

GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains

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