Version 1
: Received: 25 October 2024 / Approved: 25 October 2024 / Online: 25 October 2024 (16:17:01 CEST)
How to cite:
Alam, F.; Mohanty, S.; Almaghthawi, A.; Alnazzawi, T. S. M. Evolutionary Converging Functions (ECF) as a Novel Object Detection Method for Satellite Images. Preprints2024, 2024102066. https://doi.org/10.20944/preprints202410.2066.v1
Alam, F.; Mohanty, S.; Almaghthawi, A.; Alnazzawi, T. S. M. Evolutionary Converging Functions (ECF) as a Novel Object Detection Method for Satellite Images. Preprints 2024, 2024102066. https://doi.org/10.20944/preprints202410.2066.v1
Alam, F.; Mohanty, S.; Almaghthawi, A.; Alnazzawi, T. S. M. Evolutionary Converging Functions (ECF) as a Novel Object Detection Method for Satellite Images. Preprints2024, 2024102066. https://doi.org/10.20944/preprints202410.2066.v1
APA Style
Alam, F., Mohanty, S., Almaghthawi, A., & Alnazzawi, T. S. M. (2024). Evolutionary Converging Functions (ECF) as a Novel Object Detection Method for Satellite Images. Preprints. https://doi.org/10.20944/preprints202410.2066.v1
Chicago/Turabian Style
Alam, F., Ahmed Almaghthawi and Tahani Saleh Mohammed Alnazzawi. 2024 "Evolutionary Converging Functions (ECF) as a Novel Object Detection Method for Satellite Images" Preprints. https://doi.org/10.20944/preprints202410.2066.v1
Abstract
Low learning rates in Convolutional Neural Networks (CNNs) for image segmentation tasks can lead to convergence issues, unstable models, large oscillations, risk of divergence, sensitivity to the initial weights, and biases of the model. All these challenges make CNNs computationally expensive and require considerable training data. In contrast, achieving a high learning rate has been a significant challenge for CNNs. In response to the abovementioned challenge, this work aims to increase the learning rate of the image segmentation process. Images of any object in the universe taken from any camera can broadly be divided into spectral and geometric properties. Both these properties are prominently visible in satellite images. Therefore, if a method can classify satellite images, it can be applied to classify any image using the same technique. In this paper, Evolutionary Convergent Functions (ECF) are proposed. They convert features' spectral and geometric properties in a satellite image into mathematical equations using a decision tree and then converge with a neural network. Different high-resolution data have been chosen to extract additional features from it. This transformation process, anchored in decision tree methodology, converges with neural networks to yield unparalleled results, all while eliminating the need for computationally intensive convolutions. The proposed method extends beyond conventional boundaries by using varied high-resolution datasets. Each dataset is carefully selected with distinctive features. This research explores the untapped potential of ECF to advance the field of automated image classification, aiming to broaden the scope of current methodologies. The synergy between spectral and geometric properties emerges as a powerful combination, endowing our methods with the ability to extract nuanced and context-rich information for image analysis. The results show a high accuracy, i.e., above 90%, in almost all objects of different shapes and spectral signatures. Additionally, in terms of prediction time, ECF is faster than U-Net, a state-of-the-art method that is evidence of ECF efficiency and speed.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
