Transfer Learning Models for Land Cover and Land Use Classification in Remote Sensing Image

Abstract

ABSTRACT Land Cover or Land Use (LCLU) classification is an important, challenging problem in remote sensing (RS) images. RS image classification is a recent technology used to extract hidden information from remotely sensed images in the observed earth environment. This classification is essential for sustainable development in agricultural decisions and urban planning using deep learning (DL) methods. DL gets more attention for accuracy and performance improvements in large datasets. This paper is aimed to apply one of the DL methods called transfer learning (TL). TL is the recent research problem in machine learning and DL approaches for image classification. DL consumes much time for training when starting from scratch. This problem could be overcome in the TL modeling technique, which uses pre-trained models to build deep TL models efficiently. We applied the TL model using bottleneck feature extraction from the pre-trained models: InceptionV3, Resnet50V2, and VGG19 to LCLU classification in the UC Merced dataset. With these experiments, the TL model has been built the outdate performance of 92.46, 94.38, and 99.64 in Resnet50V2, InceptionV3, and VGG19, respectively.