Hybrid Enriched Stacked Auto Encoder and FFO-MPARCNN Algorithm for the Multispectral Image LULC Classification

Abstract

The satellite imagery classification task is fundamental to spatial knowledge discovery. Land Cover and Land usage (LULC) maps are created using a variety of image classification techniques, making it easier to conduct research on spatial and ecological processes as well as human activities. One of the most well-known applications of geographical monitoring is LULC classification. Owing to its improved feature learning and feature expression capacity, the convolutional neural network (CNN) has made several breakthroughs in feature extraction as well as classification of multispectral images in recent times as compared with conventional machine learning approaches. But on the other hand, standard CNN models have certain disadvantages, for instance, a large number of layers, which contribute to difficult computing costs. The Hybrid Enriched Stacked Auto Encoder and Pre-Activated Residual Convolutional Neural Network combined with a Fruit Fly Optimization Algorithm (HESAE-FFO-MPARCNN) has formulated where FFO used to optimise parameters and thus enhance the accuracy of classification in this work to tackle this issue. The designed FFO-MPARCNN model with its modified hyperparameters produces higher classical models as PB-RNN, ResNet and FHS-DBN for computational efficiency and accuracy of classification.