Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image

Abstract

Abstract-Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.

Hyperspectral Imaging Algorithms and Applications: A Review

“…The Spectral Angle Mapper (SAM) [12][18] [44][57] [217] algorithm is employed in hyperspectral image analysis to compare spectral data, making it valuable for tasks like image classification and target detection. SAM evaluates the similarity between a pixel's spectral signature and a reference spectrum by calculating the angle between them.…”

“…This approach significantly improves crop classification accuracy and has applications in precision agriculture and invasive species monitoring. In [217], the authors discuss the supervised classification of HSI data for identifying wheat-growing areas in Al-Kharj, Saudi Arabia. It utilizes the Parallelepiped and Spectral Angle Mapper (SAM) algorithms within ENVI software.…”