Spectral-Spatial Deep Learning for Multispectral Enhanced Image Classification and Analysis

In this study, we present a framework for spectral-spatial feature-based classification (SSFC), which employs deep learning techniques and dimension reduction to simultaneously extract spectral and spatial characteristics. The term “spectral-spatial feature-based classification” is abbreviated as “SSFC.” The acronym SSFC (Spectral-Spatial Feature-Based Categorization) stands for this method of categorization. It is suggested here to apply a method called balanced local discriminant embedding to extract spectral attributes from high-dimensional hyperspectral data sets. These features may be used to better understand the data. During the course of this experiment, multispectral image analysis (385–1050 nm) was used to take a recording of a 20-wavelength reflection of the pellets. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used in the statistical analysis that was conducted. A novel nonparametric supervised spectral-spatial classification technique for multispectral images is introduced and discussed in this study. The technique was created with the idea of applying it to the world of data imaging. The posterior probability for a multispectral image's unknown pixel will be higher if it possesses digital number (DN) vectors that are comparable to those of other pixels in the training class. This is because distinct kinds of DN vectors are thought to follow various statistical distributions in their own particular manners. The proposed method assumes that the vectors in each class have a Gaussian mixed distribution based on the statistical features of DN vectors. This is done to make the analysis simpler. For better understanding, a nonparametric adaptively Bayesian method has been devised to estimate the most precise values for the distribution model's parameters.