Crop Classification Using Different Color Spaces and RBF Neural Networks

Sandoval, Guillermo; Vázquez, Roberto A.; Garcia, Paulina; Ambrosio, Jose

doi:10.1007/978-3-319-07173-2_51

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Consequently, this work contrasts the base features for reduction using NDVI due to the analysis of features' importance for crop information extraction given in Li et al [46]. Furthermore, the HSV colour space is also compared because its performance surpassed RGB in crop classification as showed in Sandoval et al [54]. Then, these indices were evaluated for urban classes.…”

Section: Discussionmentioning

confidence: 99%

Gabor Features Extraction and Land-Cover Classification of Urban Hyperspectral Images for Remote Sensing Applications

et al. 2021

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The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time.

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Section: Discussionmentioning

confidence: 99%

Gabor Features Extraction and Land-Cover Classification of Urban Hyperspectral Images for Remote Sensing Applications

et al. 2021

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“…A nature inspired evolutionary optimisation algorithm developed based on the members of producers and scroungers are the group search optimizer [10]. In order to compute the solutions in a quick method, the fewer effective group followers will be eliminated.…”

Section: A Group Search Optimiser Algorithm -Revisitedmentioning

confidence: 99%

Brain Tumor Classification by EGSO Based RBFNN Classifier

Sharma R*,

Sungheetha,

Nuradis

2020

IJRTE

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Tumor classifier is modelled employing a proposed Enhanced Group Search Optimizer based Radial Basis Function Neural Network model is applied in this research contribution to acquire the ideal instances from the developed VOI instance an as well EGSO is utilized to optimize the weight values of the Radial Basis Function Network classifier by limiting the mean square mistake. The anticipated EGSO based RBFNN classifier brings better characterization precision and accomplished insignificant error with quicker process. The simulation results computed prove the effectiveness of the RBFNN classifier to be better in comparison with the other proposed classifiers in this thesis and that available in the literature. The proposed pattern evaluation technique presents an automatic cancer categorization procedure thru the ultimate facets which fantastic characterizes MRI brain image is benign and malignant cancers. The planned method may perhaps stretch to categorize exceptional classes of tumor (eg. Meningioma, glioma etc.,) and depth of malignancy.

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“…RBF-NN is a nonlinear and feed-forward network that is trained by a supervised algorithm (Pulido et al, 1999). RBF-NN is not only available for classification of sample varieties (Sandoval et al, 2014), but is also suitable for the quantitative analysis of mixtures (Dong et al, 2012). There are three layers in the topology structure: an input layer, a hidden layer and an output layer.…”

Section: Model Fusion Using the Rbf-nn Algorithmmentioning

confidence: 99%