Efficient Classification and Soil Quantification Using Hyperspectral Data

Abstract

Hyperspectral data accommodates a large amount of information generally in the range of 400 - 2500 nm in the electromagnetic regions. It contains the reflectance values that are captured from the measuring device. This information is high dimensional in nature, which can be utilised in several real term applications. Classification of the hyperspectral image is a prevalent domain. Most of the existing classification techniques are not able to excerpt the deep features adjacent to the hyperspectral image as it comprises of a significant number of bands. So, we proposed a deep learning-based approach to extract the deep features efficiently. This approach is a hybrid of Locality Preserving Projection, Stacked Autoencoders, and Logistic Regression. Locality Preserving Projection is a dimensionality reduction procedure that is employed to decrease the hyperspectral input. The reduced input contains the preserved local information of the input hyperspectral image. Afterward, the reduced hyperspectral image is passed to the Stacked Autoencoders for taking out the essential approximations of the input that is regarded as deep features which are then passed to the Logistic Regression for efficient construction of the prediction model. Standard hyperspectral image dataset is utilised for validating the developed model results, and it is compared with 21 machine learning models to prove the efficacy of the proposed model. Most of the existing frameworks for classification are based on spectral-spatial information. The hyperspectral image is termed as high dimensional image because it comprises of many values corresponding to the bands and adding more spatial information to already a large dataset leads to more complex dimension set. So, we proposed a pre-processing framework by using spectral values set of the hyperspectral image.