Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost.