International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 6 , Issue 2 , PP: 1-15, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Noor Edin Rabeh 1 *
Doi: https://doi.org/10.54216/IJAACI.060201
Object detection in remote sensing images (RSI) is a main procedure where the purpose is to automatically recognize and categorize certain objects or features from large-scale, remotely developed images like aerial imagery or satellite. This task role a vital play in extracting appreciated data from massive geographical regions, contributing to various applications under several domains namely environmental monitoring, urban planning, agriculture, and disaster management. Recent developments in deep learning (DL) technologies have significantly enhanced the accuracy and efficacy of object detection systems for RS, enabling more precise and automated analysis of various landscapes and facilitating informed decision-making. DL approaches namely convolutional neural networks (CNNs) are exposed to remarkable abilities in learning intricate patterns and features from difficult spatial data, resulting in enhanced accuracy and effectiveness. In this article, we present a Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning (HODC-TOADL) system. The objective of HODC-TOADL algorithm is to identify and categorize distinct types of objects that exist in the RSI. In the HODC-TOADL method, an improved Dense Net model is applied to learn the distinct features of the input RSI. Besides, the TOA has been deployed to boost the hyper parameter choice of the Dense Net method. Furthermore, the classification of objects can be carried out by employing of adaptive neurofuzzy inference system (ANFIS). The experimental evaluation of the HODC-TOADL algorithm can be studied on benchmark databases. The experimental values stated that the HODC-TOADL algorithm reaches effective classification performance compared to recent DL models.
Object Detection , Remote Sensing Images , Deep Learning , Thermal Optimization Algorithm , ANFIS
[1] Ahmed, I.; Ahmad, M.; Chehri, A.; Hassan, M.M.; Jeon, G. IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images. Remote. Sens. 2022, 14, 4107.
[2] Javadi, S.; Dahl, M.; Pettersson, M.I. Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks. IEEE Access 2021, 9, 8381–8391.
[3] Wang, J.; Teng, X.; Li, Z.; Yu, Q.; Bian, Y.; Wei, J. VSAI: A Multi-View Dataset for Vehicle Detection in Complex Scenarios Using Aerial Images. Drones 2022, 6, 161.
[4] Safarov, F.; Temurbek, K.; Jamoljon, D.; Temur, O.; Chedjou, J.C.; Abdusalomov, A.B.; Cho, Y.I. Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture. Sensors 2022, 22, 9784.
[5] Momin, M.A.; Junos, M.H.; Mohd Khairuddin, A.S.; Abu Talip, M.S. Lightweight CNN model: Automated vehicle detection in aerial images. Signal Image Video Process. 2022, 17, 1–9.
[6] Chen, Y.; Qin, R.; Zhang, G.; Albanwan, H. Spatial-temporal analysis of traffic patterns during the COVID-19 epidemic by vehicle detection using planet remote-sensing satellite images. Remote Sens. 2021, 13, 208.
[7] Wang, L.; Shoulin, Y.; Alyami, H.; Laghari, A.A.; Rashid, M.; Almotiri, J.; Alyamani, H.J.; Alturise, F. A novel deep learning—based single shot multibox detector model for object detection in optical remote sensing images. Geosci. Data J. 2022, 1–15.
[8] Ghali, R.; Akhloufi, M.A. Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation. Remote Sens. 2023, 15, 1821.
[9] Karnick, S.; Ghalib, M.R.; Shankar, A.; Khapre, S.; Tayubi, I.A. A novel method for vehicle detection in high-resolution aerial remote sensing images using YOLT approach. Multimed. Tools Appl. 2022, 109, 1–16.
[10] Wang, B.; Xu, B. A feature fusion deep-projection convolution neural network for vehicle detection in aerial images. PLoS ONE 2021, 16, e0250782.
[11] Zhang, M., Liu, L., Jin, Y., Lei, Z., Wang, Z. and Jiao, L., 2024. Tree-shaped multiobjective evolutionary CNN for hyperspectral image classification. Applied Soft Computing, 152, p.111176.
[12] Singh, P.S. and Karthikeyan, S., 2022. Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection. Remote Sensing Letters, 13(2), pp.184-195.
[13] Mahgoub, H., Albraikan, A.A., Othman, K.M., Salama, A.S., Yaseen, I. and Ibrahim, S.S., 2023. Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer with Deep Learning. IEEE Access, 11, pp.126814-126822.
[14] Han, L., Tian, J., Huang, Y., He, K., Liang, Y., Hu, X., Xie, L., Yang, H. and Huang, D., 2024. Hyperspectral imaging combined with dual-channel deep learning feature fusion model for fast and non-destructive recognition of brew wheat varieties. Journal of Food Composition and Analysis, 125, p.105785.
[15] Alajmi, M., Mengash, H.A., Eltahir, M.M., Assiri, M., Ibrahim, S.S. and Salama, A.S., 2023. Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification. IEEE Access, 11, pp.124985-124995.
[16] Chhapariya, K., Buddhiraju, K.M. and Kumar, A., 2022. CNN-Based Salient Object Detection on Hyperspectral Images Using Extended Morphology. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
[17] Zhao, Y., Zhang, Z., Bao, W., Xu, X. and Gao, Z., 2024. Hyperspectral image classification based on channel perception mechanism and hybrid deformable convolution network. Earth Science Informatics, pp.1-18.
[18] Islam, M.T., Islam, M.R., Uddin, M.P. and Ulhaq, A., 2023. A deep learning-based hyperspectral object classification approach via imbalanced training samples handling. Remote Sensing, 15(14), p.3532.
[19] Jiang, M., Feng, C., Fang, X., Huang, Q., Zhang, C. and Shi, X., 2023. Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network. Electronics, 12(3), p.508.
[20] Charchekhandra, B. (2023). Align and fusion two thermal and visual images. Pure Mathematics for Theoretical Computer Science, 1(1), 17-31.
[21] Jithendra, T., Khan, M.Z., Basha, S.S., Das, R., Divya, A., Chowdhary, C.L., Alahmadi, A. and Alahmadi, A.H., 2024. A novel QoS prediction model for web services based on an adaptive neuro-fuzzy inference system using COOT optimization. IEEE Access.
[22] Alajmi, M., Alamro, H., Al-Mutiri, F., Aljebreen, M., Othman, K.M. and Sayed, A., 2023. Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique. Remote Sensing, 15(18), p.4600.
