Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network

The accurate multi-detection of objects in satellite images has become very essential due to the high criminal activities that posed security threat to humanity all over the world. However, there are significant limitations of traditional methods of multi-object detection such as matching based techniques and object based image analysis. Although Convolutional neural network and image processing techniques has been proved to be essential fields in so many applications of computer vision specifically multi-object detection, multi-object classification, object retrieval, object recognition and object segmentation in a digital image or video, however, multi-object detection especially in satellite images suffer from problems such as shadow, camouflage, and occlusion. The aim of this research work was to design a robust multi-class object detection model in satellite images using image processing techniques and convolutional neural network with a particular concern on image preprocessing, image denoising and image enhancement to enable address the issue of noise in satellite images. The Satellite image that are propose for this model is LandSat-8, because it is free access for research and have a tract record in terms of consistency. The proposed model applied supervised learning algorithm for training different samples of labeled data for the model to enable the system detect vegetation, water bodies, road networks and building. This research will enable the government to know the positions as well as the coordinates of every thick forest, drainage, road networks and buildings in the forest for security reasons. It is at the heart of this research to pave away for the full implementation of this model using either MATLAB or Python Programming.

Convolutional Neural Network, Computer Vision, Object Detection, Satellite, Image Processing, Digital Image, Camouflage and Occlusion

Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. (2022). Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network. Communications, 9(1), 1-5.

Copyright © 2021 Authors retain the copyright of this article.
This article 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.

1. Lincheng, J., Fan, Z., Fan, L., Shuyuan, Y., Zhixi, F. & Rang, Q. (2019). A survey of deep learning-Based object detection. IEEE Access Multidisciplinary. 7, 128837-128867.
2. Feng, H., Sui, W., Huang, C., Xu, L. & Ki, A. (2019). Water body extraction from very high resolution remote sensing imagery using deep U-net and super-pixel based conditional random field model. IEE Geoscience. Remote Sensing Letter, 16 (4), 618-622.
3. Li, Y., Xu, Rao, J., Guo, L. L., Yan, Z., & Jin, S. A. (2019). Y-Net deep learning method for road segmentation using high-resultion visible remote sensing image. Remote sensing letter 10, 381-390.
4. Guoji W., Wu, M., Wei, X. & Song, H. (2020). Water identification from high resolution remote sensing image based on multidimensional densely connected convolutional neural networks, Remote Sensing, 12 (5), 795.
5. Mengya, L., Penghai, W., Biao, W., Honhlyun, P., Hui, Y. & Yanlan, W. (2021). A Deep learning method of water body extraction from high resolution remote sensing images with multisensors. IEEE Journal of Selected Tropics in applied Earth observation and remote sensing, 14, 3120-3132.
6. Gao, L., Song, W., Dai, J., & Chen, Y., (2019). Road extraction from high-resolution remote sensing imagery using refined Deep Residual Convolutional neural network Remote sensing. 11, 553.
7. Alexander, A. S. G., Ilma, A. & Edy, I. (2020). Semantic segmentation of Aerial imagery for road Extraction with deep learning, ICIC Express letter, 14 (1), 43-51.
8. Ji, S., Wei, S., & Lu, M. (2019). A scale robust convolutional neural network for automatic.
9. Geesara, P. & Ilya, A. (2018). Deep Learning Approach for Building Detection in Satellite Multispectral Imagery, IEE International Conference on Intelligent Systems Sep. 2018.
10. Nahhas, F. H., Shafri, H. Z., Sameen, M. I. Pradhan, B. & Mansor, S. (2018). Deep learning approach for building detection using LIDAR-orthophoto fusion. Journal of Sensor 3 (6), 1-9.
11. Arshitha, F. & Biju, K. S. (2020). Accurate detection of building from satellite images using CNN. In Proceeding of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE) 12-13 June 2020, Istanbul, Turkey.
12. Vakalopoulou, M., Karantzalos, K., Komodakis, N., & Paragios, N. (2015). Building detection in Very high-resolution multispectral data with deep learning features. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International 1873-1876.
13. Li, S., Yuqi, T. & Liangpei, Z. (2017). Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model, IEEE Geoscience and Remote Sensing Letters, 14 (11), 1998-2002.
14. Ma, L., Liu Y., Zhang, X., Ye, Y. and Honson, J. (2019) Deep learning in remote sensing application a meta-analysis and review. ISPRS Journal of Photogrammetric and remote sensing magazine 152, 166-177.
15. Li, Y., Zhang, H., Hue, X., Jiang, Y. and Shen, Q. (2018) Deep learning for remote sensing image classification a survey. Wiley Interdisciplinary review data mining and knowledge discovery 8 (6) 1264.