Enhancing the Convolutional Neural Nets for Analysing the Aerial and Satellite Image Interpretation

Abstract

This research investigates the use of Convolutional Neural Networks (CNNs) for examining aerial and satellite images utilizing Tensor Flow. As high-resolution remote sensing data becomes increasingly accessible, there is an increasing demand for precise and automated analysis techniques. We employ CNNs to detect and classify complex patterns in these images, especially for land cover classification tasks. The system has been trained and validated using an extensive dataset of labeled aerial and satellite images, guaranteeing its dependability and precision across different situations. By leveraging Tensor Flow, we take advantage of its robust computational capabilities and scalability, which facilitate the development and deployment of sophisticated neural network models. The results show significant improvements in both accuracy and processing speed compared to traditional image analysis techniques. This approach has important implications for areas such as urban planning, disaster management, and environmental conservation, highlighting the transformative role of deep learning in remote sensing.

Introduction

I. Introduction

Convolutional Neural Networks (CNNs) have transformed the analysis of aerial and satellite imagery by automatically extracting hierarchical features for applications such as land use classification, object recognition, and change detection. These capabilities, integrated with deep learning, enable better data handling and analysis for environmental monitoring, urban planning, and disaster management. Data Science combines domain expertise with programming and statistical skills to extract insights from structured and unstructured data, influencing decision-making across industries. Artificial Intelligence (AI) mimics human intelligence to perform tasks like language processing, computer vision, and recommendation systems. Deep Learning, a subset of machine learning based on artificial neural networks, enables hierarchical feature learning and is especially effective for large-scale image interpretation tasks.

II. Literature Review

• Orbital Edge Computing (OEC): Addresses LEO satellite limitations using a greedy-based task allocation algorithm to optimize delay and energy use, outperforming traditional methods.

• Object Discovery for Image Retrieval: Introduces an unsupervised method to improve region-based image retrieval using deep features and self-boosting without manual labels.

III. Existing System

A simulation system for LEO constellations uses elite strategic genetic algorithms (ESGA) to optimize satellite placement. Due to LEO congestion and 6G latency demands, the system proposes VLEO (Very Low Earth Orbit) constellations to maximize coverage and minimize the number of satellites. Existing methods depend on simulated data, limiting real-world accuracy. Deep neural networks and universal approximation theories support the design, but challenges include space debris, high deployment complexity, and scalability issues.

Drawbacks:

1. Space debris in LEO.

2. Difficulty meeting ultra-low latency for 6G.

3. High complexity for VLEO deployment.

IV. Proposed System

The new system uses CNNs for satellite/aerial image analysis with:

• TensorFlow for CNN model development.

• Django for deploying a web interface where users upload images for real-time classification.

• Applications include urban development, disaster management, and environmental monitoring.

• Data Flow Diagrams (DFD) and class diagrams illustrate system design.

• Advantages: Real-time processing, deep learning integration, and wide applicability.

V. Methodology

• Workflow: Collect diverse image datasets → preprocess → design/select CNN (e.g., ResNet, VGG) → train using supervised learning with performance evaluation (accuracy, precision, recall, IoU) → deploy for real-world use.

• Design tools: Class diagrams, use case diagrams, Entity Relationship Diagrams (ERDs).

• Software tools:

  • Anaconda Navigator: Simplifies environment and package management.

  • Jupyter Notebook: Interactive environment for data analysis and model development.

• Model Training: Utilizes CNN layers like Convolution2D, MaxPooling2D, Dense. Dropout and early stopping prevent overfitting. Various architectures (Manual CNN, Xception, DenseNet) are tested and compared.

• Keras Model Creation:

  1. Sequential API – Simple stack of layers.

  2. Functional API – Supports complex, multi-input/output models.

  3. Model Subclassing – Custom-built from scratch.

Conclusion

The exploration of Convolutional Neural Networks (CNNs) for aerial and satellite image interpretation demonstrates their immense potential in enhancing the accuracy and efficiency of analyzing vast amounts of geospatial data. By leveraging CNNs, we can automate the detection and classification of various landforms, urban developments and environmental changes, which are crucial for applications ranging from urban planning to disaster management. The ability of CNNs to extract intricate features from higher solution images offers a significant advancement over traditional methods, paving the way for more precise and timely decision making processes in remote sensing and geospatial analysis. This approach not only accelerates the processing of satellite imagery but also contributes to the growing field of AI-driven Earth.

References

[1] Yuru Zhang, Chen Chen, Lei Liu, Dapeng Lan, Hongbo Jiang, Shaohua Wan “Aerial Edge Computing on Orbit: A Task offloading and allocation Scheme.” IEEE Transcations on Network Science and Engineering,vol.10,pp. 275-285,2023. [2] M. Zhao, C. Chen, L. Liu, D. Lan and S. Wan, \"Orbital collaborative learning in 6G space-air-ground integrated networks\", Neurocomputing, vol. 497, pp. 94-109, 2022. [3] M. Goudarzi, H. Wu, M. S. Palaniswami and R. Buyya, \"An application placement technique for concurrent IoT applications in edge and fog computing environments\", IEEE Trans. Mobile Comput., vol. 20, no. 4, pp. 1298-1311, Apr. 2021. [4] Y. Wang, J. Yang, X. Guo and Z. Qu, \"A game-theoretic approach to computation offloading in satellite edge computing\", IEEE Access, vol. 8, no. 1, pp. 12510-12520, 2020. [5] A. Guidotti, B. Evans and M. Di Renzo, \"Integrated satellite-terrestrial networks in future wireless systems\" in Int. J. Satell. Commun. Netw., vol. 37, pp. 73-75, 2019. [6] M. A. López Peña and I. Muñoz Fernández, \"SAT-IoT: An architectural model for a high-performance fog/edge/cloud IoT platform\" in Proc. IEEE 5th World Forum Internet Things, Limerick, Ireland, vol. 1, pp. 633-638, 2019. [7] H. El-Sayed, S. Sankar, M. Prasad, D. Puthal, A. Gupta, M. Mohanty, et al., \"Edge of Things: The big picture on the integration of edge IoT and the cloud in a distributed computing environment\", IEEE Access, vol. 6, pp. 1706-1717, 2018 [8] C. Sun, Y. Zhang, L. Wang and W. Zhao, \"Analysis on adaptability of ground 5G technology in LEO constellation\", Radio Eng., vol. 48, no. 3, pp. 167-172, 2018. [9] P. Porambage, J. Okwuibe, M. Liyanage, M. Ylianttila and T. Taleb, \"Survey on multi-access edge computing for Internet of Things realization\", IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 2961-2991, 2018. [10] L. Boero, R. Bruschi, F. Davoli, M. Marchese and F. Patrone, \"Satellite networking integration in the 5g ecosystem: Research trends and open challenges\", IEEE Netw., vol. 32, pp. 9-15, Sep. 2018. [11] T. Ahmed et al., \"Software-Defined Satellite Cloud RAN\", lntl J. Satellite Commun. and Networking, vol. 36, no. 1, pp. 108-33, 2018. [12] J. Liu et al., \"Joint Placement of Controllers and Gateways in SDN-Enabled 5G-Satellite Integrated Network\", IEEE JSAC, vol. 36, no. 2, pp. 221-32, Feb. 2018. [13] M. Sheng et al., \"Toward a Flexible and Reconfigurable Broadband Satellite Network: Resource Management Architecture and Strategies\", IEEE Wireless Commun., vol. 24, no. 4, pp. 127-33, Aug. 2017. [14] J. Ordóñez-Lucena et al., \"Network Slicing for 5G with SDN/NFV: Concepts Architectures and Challenges\", IEEE Commun. Mag., vol. 55, no. 5, pp. 80-87, May 2017. [15] Z. Qu, G. Zhang, H. Cao and J. Xie, \"LEO satellite constellation for Internet of Things\", IEEE Access, vol. 5, pp. 18391-18401, 2017. [16] R. Ferrús et al., \"SDN/NFV-Enabled Satellite Communications Networks: Opportunities Scenarios and Challenges\", Physical Commun., vol. 18, pp. 95-112, 2016. [17] B. Yang et al., \"Seamless Handover in Software-Defined Satellite Networking\", IEEE Commun. Letts., vol. 20, no. 9, pp. 1768-71, 2016 [18] Z. Ma et al., \"Key Techniques for 5G Wireless Communications: Network Architecture Physical Layer and MAC Layer Perspectives\", Science China Info. Sciences, vol. 58, no. 40, pp. 1-2, Apr 2015. [19] L. Bertaux et al., \"Software Defined Networking and Virtualization for Broadband Satellite Networks\", IEEE Commun. Mag., vol. 53, no. 3, pp. 54-60, Mar. 2015. [20] B. G. Evans, \"The role of satellites in 5G\", Proc. IEEE 7th Adv. Satell. Multimedia Syst. Conf. 13th Signal Process. Space Commun. Workshop, vol. 1, pp. 197-202, 2014.

Copyright

Copyright © 2025 Komathi D, Swathi S, Monisha A, Dr. Swapna S. 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.