Utilizing Hybrid Time Series Clustering Algorithms for Hyper Spectral Image Processing

Hyperspectral photo processing (HIP) is an analytical method for recognizing and examining features in excessive-dimensional record sets. One of the demanding situations faced with the aid of HIP is the presence of noisy capabilities that may make it challenging to understand actual statistics and degrade the accuracy of the evaluation. A hybrid time series clustering technique has been proposed to symbolize and categorize noisy hyperspectral photos. This approach combines two different clustering algorithms (self-organizing map (SOM) and hierarchical clustering (HC)) with signal compressors (wavelet remodel (WT) and discrete cosine transform (DCT)) to come across and reduce noise. This approach has been proven to have better accuracy than traditional methods for hyperspectral photo processing. It also enables higher detection of features and offers a more accurate representation of the facts set, permitting researchers to higher hit upon subtle functions that conventional strategies may forget.