Unsupervised Classification of Hyper Spectral Images using Feature Extraction and Fuzzy Logic

Abstract

Hyperspectral pictures are complicated information items with high spectral resolution, making their categorization and analysis timeingesting and challenging. Traditional strategies for classifying hyperspectral pix may be unreliable and gradually attributable to the presence of diverse noise resources and a high range of pixels. This paper proposes a new unsupervised classification approach for hyperspectral pictures using function extraction and fuzzy common sense. The method starts by first using feature extraction techniques on the hyperspectral pictures to lessen the dimensionality of the facts. Numerous characteristic extraction algorithms, including primary thing analysis (PCA) and impartial component evaluation (ICA), are tested to determine which function extraction algorithms yield satisfactory effects. The reduced function area is then used as an entry for the fuzzy category system. The bushy common sense device is used to classify the hyperspectral pix into distinctive classes according to the extracted capabilities. Experimental results display that the proposed method achieves proper effects for the category venture with classification accuracy accomplishing as high as 79%. The proposed technique demonstrates advanced performance over conventional category strategies in terms of each accuracy and speed. Hyperspectral pics (HSI) offer valuable statistics approximately the environment and the functions gift inside it. But, the sheer quantity of facts present in HSI makes guide evaluation of those photos a time-eating and exhausting project. As such, there is a growing demand for robust and reliable automated techniques to analyze HSI. In this context, unsupervised tactics for classifying HSI have gained interest due to their ability to examine facts without requiring manually categorized education facts. Fuzzy logic is one method being explored for unsupervised HSI type due to its capability to assign more than one label to pixels of the image and its robustness to noise. Right here, the HSI picture is first pre-processed and feature extracted to produce a fixed of numerical statistics that may be used to classify the pixels of the image extra as they should be. This feature extracted records are then used as enter to a fuzzy inference gadget, which tactics the enter values using fuzzy good judgment operators and linguistic policies to provide crisp, numerical output values that define the class label of every pixel. by way of enforcing fuzzy good judgment primarily based strategies for HSI category, the difficulty of high complexity may be addressed as the unambiguous output of the bushy common sense gadget simplifies the information evaluation mission.