Abstract
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.
Highlights
Introduction and Ying ZhangHyperspectral images can register the energy recorded through the electromagnetic spectrum in a single data structure, allowing the representation of the captured objects in signals that provide information beyond the human eye’s perception
The results show that the evaluation with support vector machine (SVM) last at least 1 × 102 times the evaluation performed with NNMLP-11_22
The study of Seng and Ang [28] was taken into consideration to select the Linear Discriminant Analysis (LDA) method to reduce the extracted features because their experiments showed a great advantage of using LDA over principal component analysis (PCA) in face recognition datasets
Summary
Hyperspectral images can register the energy recorded through the electromagnetic spectrum in a single data structure, allowing the representation of the captured objects in signals that provide information beyond the human eye’s perception These images can be used for different applications, for example soil evaluation for crop health and protection [1], maritime traffic surveillance [2], detection of minerals presence [3] and urban characterisation [4]. The acquisition of this kind of image has presented time and cost reduction in recent years due to the improvement in portable technologies and transmission platforms [5,6] These advances have led to the processing of a significant volume of data growing at an accelerated speed, which originates data processing challenges [7]. It benefits from the analysis of the hidden correlation in the large volume of data, resulting in functional knowledge [11]
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