Feature level fusion for land cover classification with landsat images: A hybrid classification model

Abstract

Classification of land cover using satellite images was a major area for the past few years. A raise in the quantity of data obtained by satellite image systems insists on the requirement for an automated tool for classification. Satellite images demonstrate temporal or/and spatial dependencies, where the traditional artificial intelligence approaches do not succeed to execute well. Hence, the suggested approach utilizes a brand-new framework for classifying land cover Histogram Linearisation is first carried out throughout pre-processing. The features are then retrieved, including spectral and spatial features. Additionally, the generated features are merged throughout the feature fusion process. Finally, at the classification phase, an optimized Long Short-Term Memory (LSTM) and Deep Belief Network (DBN) are introduced that portrays classified results in a precise way. Especially, the Opposition Behavior Learning based Water Wave Optimization (OBL-WWO) model is used for tuning the weights of LSTM and DBN. Atlast, many metrics illustrate the new approach’s effectiveness.