Classification of multispectral images through a rough-fuzzy neural network
نویسندگان
چکیده
Shao-Han Liu Jzau-Sheng Lin, MEMBER SPIE National Chin-Yi Institute of Technology Department of Electronic Engineering No. 35, Lane 215, Sec. 1, Chung-Shan Rd Taiping, Taichung, Taiwan E-mail: [email protected] Abstract. A new fuzzy Hopfield-model net based on rough-set reasoning is proposed for the classification of multispectral images. The main purpose is to embed a rough-set learning scheme into the fuzzy Hopfield network to construct a classification system called a rough-fuzzy Hopfield net (RFHN). The classification system is a paradigm for the implementation of fuzzy logic and rough systems in neural network architecture. Instead of all the information in the image being fed into the neural network, the upperand lower-bound gray levels, captured from a training vector in a multispectal image, are fed into a rough-fuzzy neuron in the RFHN. Therefore, only 2/N pixels are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance. © 2004 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.1629685]
منابع مشابه
Fuzzy Neural Network Models For Multispectral Image Analysis
Fuzzy neural networks (FNNs) provide a new approach for classification of multispectral data and to extract and optimize classification rules. Neural networks deal with issues on a numeric level, whereas fuzzy logic deals with them on a semantic or linguistic level. FNNs synthesize fuzzy logic and neural networks. Recently, there has been growing interest in the research community not only to u...
متن کاملClassification of Multispectral Images Based on a Fuzzy-Possibilistic Neural Network
In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic -means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fu...
متن کاملLand Cover Classification from SPOT Multispectral And Panchromatic Images Using Neural Network Classification of Fuzzy Clustered Spectral and Textural Features
A technique is described for doing land cover classification using a neural network to integrate and classify SPOT multispectral and derived texture data. Orientated texture energy was derived from the higher spatial resolution SPOT panchromatic band with directional spatial filtering techniques. The multispectral and textural data were each clustered using a reported fuzzy learning vector quan...
متن کاملMultispectral Image Analysis Using Random Forest
Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman propo...
متن کاملStudy on the Trend of Range Cover Changes Using Fuzzy ARTMAP Method and GIS
The major aim of processing satellite images is to prepare topical and effectivemaps. The selection of appropriate classification methods plays an important role. Amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. In present study, TM images of 1987, and ETM+ images of 2000 and 2006were analyzed using artificial fuzzy ARTMAP neural netwo...
متن کامل