Land use Land Cover Classifıcation using Deep Learning Classifiers for Remote Sensed Images

Abstract

The image classification accuracy is enhanced by applying Deep Learning Models (DLM) which has a robust learning ability by incorporating both feature extraction and classification procedure into single image classification test. Here a deep learning-based classification technique is applied to High Spatial Resolution Remote Sensing Images (HSRRSI) to extract multi-layer features. The two networks i.e., residual network and inception network are combined into one new model to obtain higher accuracy then said individual residual network and inception network. The new model designed was extensively weighed on data’s from Remote Sensing Image Classification Benchmark (RSI-CB). The dataset obtained from RSI-CB is split into 70:30 ratio for training and testing respectively. The performances of proposed approach are then assessed by kappa coefficient (K) and accuracy (A).