Object Detection and Classification of Hyperspectral Images Using K-NN

Abstract

This Object detection and classification using Hyperspectral images is a critical aspect of remote sensing and computer vision. This technology involves identifying objects of interest within an image and classifying them based on their spectral signatures. Hyperspectral imaging provides a more detailed representation of objects compared to traditional color images, enabling more precise classification. The increased accuracy and reliability provided by this technology make it useful in a range of applications, such as environmental monitoring, military surveillance, and agriculture. However, object detection and classification in hyperspectral images can be challenging due to the large size of the data and the complexity of the algorithms involved. Nevertheless, ongoing research in this area continues to improve the performance of object detection and classification using hyperspectral images. In this paper, we are utilizing the K-Nearest Neighbor algorithm as part of the research work to determine the accuracy of our model.