Texture feature performance for image segmentation
Pattern Recognition
Neural Networks
Application of an artificial neural network to landcover classification of thematic mapper imagery
Computers & Geosciences - Artificial intelligence applications in geoscience
MRF Clustering for segmentation of color images
Pattern Recognition Letters
Natural Object Classification Using Artificial Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Mapping multi-spectral remote sensing images using rule extraction approach
Expert Systems with Applications: An International Journal
Dynamic learning of SCRF for feature selection and classification of hyperspectral imagery
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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During the past decade, there have been significant improvements in remote sensing technologies, which have provided high-resolution data at shorter time intervals. Considerable effort has been directed towards developing new classification strategies for analysing this imagery, but the use of artificial intelligence-based analysis techniques has been somewhat limited. The aim of this study was to develop an artificial neural network (ANN)-based technique for the classification of multispectral aerial images for land use in agricultural and environmental applications. The specific land-use classes included water, forest, and several types of agricultural fields. Multispectral images at a 1-m resolution were obtained for the state of Georgia, USA from a Geographic Information Systems (GIS) data clearinghouse. These false-colour images contained green, red and infrared true-colour information. Three approaches were used for the preparation of the inputs to the ANN. These included histograms of the pixel intensities, textural parameters extracted from the image, and matrices of the pixels for spatial information. A probabilistic neural network was used. Seven hundred images were used for model development and 175 for independent model evaluation. The overall accuracy for the evaluation data set was 74% for the histogram approach, 71% for the spatial approach and 89% for the textural approach. The evaluation of ANNs based on various combinations of all three approaches did not show an improvement in accuracy. We also found that some approaches could be used selectively for certain classes. For example, the textural approach worked best for forest classes. For future studies, edge detection prior to classification, with more careful selection of each class, should be included for land-use classification of multispectral images.