International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 6, December 2023, pp. 6882~6890 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6882-6890  6882 Journal homepage: http://ijece.iaescore.com Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro- temporal data for land cover classification Muhammad Hasanat 1,2 , Waleed Khan 1,2 , Nasru Minallah 1,2 , Najam Aziz 1,2 , Awab-Ur-Rashid Durrani 1,2 1 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Khyber Pakhtoonkhwa, Pakistan 2 National Center of Big data and Cloud Computing, University of Engineering and Technology Peshawar, Peshawar, KP Pakistan Article Info ABSTRACT Article history: Received Nov 11, 2022 Revised Apr 20, 2023 Accepted Apr 24, 2023 Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning- based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm. Keywords: Artificial neural network Deep learning Neural networks Remote sensing Transfer learning This is an open access article under the CC BY-SA license. Corresponding Author: Nasru Minallah Department of Computer Systems Engineering, University of Engineering and Technology Peshawar Peshawar, KP, Pakistan Email: n.minallah@uetpeshawar.edu.pk 1. INTRODUCTION In recent times, the field of deep learning has gained immense popularity and has become a central topic in big data research due to its superior performance compared to traditional machine learning algorithms. This has resulted in its successful application in a variety of fields, such as image identification [1], natural language processing [2], and speech enhancement [3]. The use of deep learning models in remotely sensed images has also become increasingly popular, with models such as ResNet, AlexNet, and capsule network exhibiting remarkable performance when trained with ample labelled data. However, creating large-scale, well- labelled datasets for remote sensing is challenging due to the high cost associated with data collection and annotation [4]. Furthermore, the increasing availability of large amounts of data from advanced satellite sensors has led to newly collected remote sensing data often lacking labelled information, posing a challenge to deep