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Hyperspectral Imaging (HSI) Has Emerged As A Powerful Remote Sensing Technique Due To Its Ability To Capture Detailed Spectral Information Across Hundreds Of Narrow And Contiguous Wavelength Bands. This Rich Spectral Resolution Enables Precise Identification And Discrimination Of Surface Materials, Which Is Critical In Applications Such As Agriculture, Mineral Exploration, Environmental Monitoring, And Defense. Traditional Classification Methods Often Face Challenges In Handling The High Dimensionality And Spectral Similarity Inherent In Hyperspectral Data. To Address This, Maximum Abundance Classification (MAC) Is Employed As An Effective Technique For Hyperspectral Image Analysis. MAC Is Based On Spectral Unmixing, Where Each Pixel Is Represented As A Mixture Of Multiple Endmembers, And Classification Is Performed By Assigning A Pixel To The Class Corresponding To The Endmember With The Highest Abundance Fraction. This Method Not Only Improves Classification Accuracy But Also Provides Insights Into The Sub-pixel Composition Of Heterogeneous Regions. The Proposed Work Focuses On Implementing MAC For Hyperspectral Image Datasets, Highlighting Its Effectiveness In Resolving Mixed Pixels And Enhancing Material Discrimination Compared To Conventional Pixel-based Classifiers. The Results Demonstrate That Maximum Abundance Classification Offers A Robust Approach For Extracting Meaningful Information From Hyperspectral Images, Making It Highly Suitable For Real-world Remote Sensing Applications.