Probabilistic neural network architecture for high-speed classification of remotely sensed imagery

Chettri, Samir R.; Cromp, Robert F.

doi:10.1016/0736-5853(93)90024-x

Cited by 27 publications

(4 citation statements)

References 21 publications

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“…al. 1989, Campbell 1989, and Chettri and Cromp 1993]. These efforts have clearly shown that neural networks have performed as well as the statistical classifiers if not better.…”

Section: Meta-data Extractionmentioning

confidence: 97%

<title>Extracting an image similarity index using meta-data content for image mining applications</title>

Raghavan¹,

Cromp

Srinivasan³

et al. 1997

25th AIPR Workshop: Emerging Applications of Computer Vision

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The motivation behind this research has been to identify, and where possible, minimize or eliminate potential problem areas facing NASA in its mission of gathering and analyzing remotelysensed imagery in both Earth and space disciplines. Managing and extracting useful information from the massive image databases resulting from such missions is a challenging task for NASA.The key to real-time archival and retrieval of this massive image data lies in the notion of content based image data management. Two major steps are involved in this process. The first one is to automatically extract image content or meta-data from satellite imagery. The second one is to organize this database to permit users from numerous disciplines and communities to access data relevant to their needs. Accordingly, each data set is indexed in multiple ways, enabling users to retrieve images by specifying constraints over a combination of attributes. One such method provides users the ability to search the data holdings using a metric of similarity in content so that neighboring images in the database have a high probability of hit when queried for a specific type of meta-data content. An important area of research is therefore to compute and evaluate similarity measures for images. In this paper, we present a backpropagation neural network based technique to classify a multispectral satellite image and extract a similarity measure using the meta-data classification content.

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“…al. 1989, Campbell 1989, and Chettri and Cromp 1993]. These efforts have clearly shown that neural networks have performed as well as the statistical classifiers if not better.…”

Section: Meta-data Extractionmentioning

confidence: 97%

<title>Extracting an image similarity index using meta-data content for image mining applications</title>

Raghavan¹,

Cromp

Srinivasan³

et al. 1997

25th AIPR Workshop: Emerging Applications of Computer Vision

View full text Add to dashboard Cite

show abstract

“…Nevertheless, its major drawback is on the need to store all training cases in the pattern layer for future classifications. Applications of PNN are found in various areas such as image processing and medical diagnosis (see [1,2,11,13,46,50,59] for example).…”

Section: Pnn Modelmentioning

confidence: 99%

On detecting nonlinear patterns in discriminant problems

Tsai

2006

Information Sciences

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“…A description of the derivation of the PNN classifier was given in Chettri and Cromp (1993). PNNs had been used for classification problems.…”

Section: Design and Implementation Of The Classification System Based On Pnnmentioning

confidence: 99%

Human influenza: a virus classification using a probabilistic neural network

2008

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Purpose -The traditional method to distinguish the serotype of influenza A virus was based on the antigen reaction of HA and NA with their antibodies. The antibody of specific subtype virus was difficult to get and the reaction was not easy to be done. To be a complementation to the traditional classification methods based on the serological reaction, aims to present a novel method for viral classification. Design/methodology/approach -The similarity values of all subtype HA genes in vector space were considered and classified using a probabilistic neural network (PNN). The PNN model was trained by the 132 viral sequences in the training set and the classification quality was examined using 28 viral sequences in the testing set. Findings -A novel technique for the serotype classification of influenza A virus isolated from human was proposed in the paper. The system achieved 100 per cent accuracy with all serotypes of human influenza A virus.Research limitations/implications -The time for the large-scale calculations of the average similarity based on multisequence alignment is the main limitation. Practical implications -This is a supplementation to the traditional virology research. Originality/value -The novel classification method based on the similarity of viral nucleotide sequences and the PNN model would be useful for the epidemic supervision and prevention.

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Probabilistic neural network architecture for high-speed classification of remotely sensed imagery

Cited by 27 publications

References 21 publications

<title>Extracting an image similarity index using meta-data content for image mining applications</title>

<title>Extracting an image similarity index using meta-data content for image mining applications</title>

On detecting nonlinear patterns in discriminant problems

Human influenza: a virus classification using a probabilistic neural network

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