With the update of remote sensing equipment and the improvement of technology, remote sensing images not only have advantages in quantity, but also improve in imaging quality. Convolutional neural network has been widely used in object detection, which can extract targets from a large number of images and has a very powerful feature extraction capability. The detection technology for remote sensing images has a promoting role. This paper introduces the application of remote sensing image, analyzes several common convolutional neural network technologies and features, and analyzes the application of convolutional neural network in remote sensing images. Through comparative analysis, the efficiency of convolutional neural network technology is relatively high in remote sensing image target detection, which indeed solves the problems of low detection accuracy and high miss detection rate of remote sensing targets, remote sensing target characteristics and mainstream algorithms in current remote sensing images.

Meiyan Li, Joey S. Aviles. Research on remote sensing image object detection based on convolutional neural network. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 131-137. https://doi.org/10.25236/AJCIS.2023.060717.

[1] Yang Y & Newsam S(2021).Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems.ACM, 2021:270-279.

[2] Lazebnik S & Schmid C(2021).Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. IEEE Computer Society Conference on Computer Visi Vaduva C et al (2019). Latent Dirichlet allocation for spatial analysis of satellite images. IEEE Transactions on Geoscience and Remote sensing, 51(5): 2770-2786.

[3] Joao Carreira, Fuxin Li & Cristian Sminchisescu.Object Recognition by Sequential Figure-Ground Ranking, In IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 2169–2178).

[4] Szegedy C et al(2021).Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition.1-9.

[5] He K et al(2021).Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.770-778.

[6] Penatti O A B et al(2021). Do deep features generalize from everyday objects to remote sensing and aerial scenesdomains?Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2015: 44-51.

[7] Khan A et al(2022).A Survey of the Recent Architectures of Deep Convolutional Neural Networks. Computer Vision and Pattern Recognition, 2022.

[8] LecunY et al(2019).Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1(4):541-551.

[9] Deng, L. and Yu, D. (2013) .Deep Learning: Methods and Applications. Foundations and Trends? in Signal Processing, 7, 197-387.

[10] Lecun Y et al (2019). Deep learning. nature,521(7553):436-444.

[11] Scherer D et al(2021).Evaluation of pooling operations in convolutional architectures for object recognition. international conference on artificial neural networks,92-101.

[12] Gu J et al(2021). Recent advances in convolutional neural networks. Pattern Recognition, 354-377.

[13] Xu B et al(2022). Empirical Evaluation of Rectified Activations in Convolutional Network. Computer Science,2022.

[14] Loffe S & Szegedy C(2021).Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift. international conference on machine learning,448-456.

[15] Lin M et al (2019). Network In Network. arXiv: Neural and Evolutionary Computing, 2019.

[16] Dahl G E et al(2021).Improving deep neural networks for LVCSR using rectified linear units and dropout. international conference on acoustics, speech, and signal processing, 8609-8613.

[17] Dauphin Y N et al(2020).Equilibrated adaptive learning rates for non-convex optimization. neural information processing systems, 1504-1512.

[18] Redmon J et al (2019).You Only Look Once:Unified,Real-Time Object Detection. IEEE, 2016.

[19] Zoph B & Le Q V(2020). Neural Architecture Search with Reinforcement Learning. arXiv: Learning, 216-287.

[20] Zoph B et al (2019).Learning Transferable Architectures for Scalable Image Recognition. computer vision and pattern recognition,8697-8710.

[21] Zoph B & Neumann M(2019).Progressive Neural Architecture Search. european conference on computer vision, 19-35.

[22] JunhuaFang et al (2020).Distributed and parallel processing for real-time and dynamic spatio-temporal graph. World Wide Web: Internet and Web Information Systems,23(9).234-256.

[23] Hao Wang et al(2020). A diffusion algorithm based on P systems for continuous global optimization. Journal of Computational Science,44-65.