In recent years, the various Deep Learning architectures have been applied in fields such as speech recognition, natural language processing, and many more classification tasks, where they have usually undergoing the traditional methods. The motivation for such an idea is inspired by the fact that the human brain is organized in a deep architecture, with a given input percept represented at several levels of abstraction. Previous research has problems like – complex unstructured data of satellite images to be observed in short period of time & result is misjudged. So, it is difficult to obtain accurate result immediately. Therefore, proposed paper addresses need of Convolutional Neural Network (CNN) for automatic object detection in military & space image. As per study of CNN we conclude as it is for accurate classification of object from image.

Convolutional Neural Network (CNN); Features; Kernels; Pooling; Rectified Linear Unit (ReLU ), etc

[1] S. V. Shinde and U. V. Kulkarni, “Exctracting Classification Rules from Modified Fuzzy Min-Max Neural Network for Data with Mixed Attributes,” Elsevier Journal -Applied Soft Computing Journal, pp. 364, 2016.
[2] Asha Rathee, “Satellite Image Classification using Ant Colony Optimization and Neural Network”, in International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 1, Issue 3 , pp. 76, 2017.
[3] S. V. Shinde and U. V. Kulkarni, “Extended Fuzzy Hyperline-Segment Neural Network with Classification Rule Extraction”, Article in Press, Neurocomputing, April 2017.
[4] Parminder Kaur Birdi, Karbhari Kale, “Deep Convolutional Neural Network Models for Land Use and Land Cover Identification Using Dataset Created From LISS-IV Satellite Images”, in International Journal of Scientific Research in Computer Science, Engineering and Information Technology,Vol. 3, Issue 3, pp. 2123, 2018.
[5] Kuang ping, cao wei-na, wu qiao. “Preview on structures and algorithms of deep learning”. In IEEE, pp. 176, 2014.
[6] Nguyen Kien, Fookes Clinton, Sridharan Sridha. “Improving deep convolutional neural networks with unsupervised feature learning”. In IEEE 978-1-4799-8339-1/15/2015.
[7] Albawi saad .”Understanding of a Convolutional Neural Network”. In IEEE 978-1-5386-1949-0/17/ 2017.
[8] Dahl George E., Sainath Tara N., Hinton Geoffrey E.” Improving deep neural networks for lvcsr using rectified linear units and dropout”. In IEEE 978-1-4799-0356-6/13, pp. 8609, 2013.
[9] Liu Lingqiao, Shen Chunhua, Hengel Anton van den.”Cross-Convolutional-Layer Pooling for Image Recognition”. In IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 11,pp. 2305, november 2017.
[10] Sun Deqing, Wulff Jonas, Sudderth Erik B., Pfister Hanspeter, Black Michael J.” A Fully-Connected Layered Model of Foreground and Background Flow”. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2451, 2013.
[11] Hiippala Tuomo. Recognizing military vehicles in social media images using deep learning. In IEEE, 978-1-5090-6727-5/17/$31.00, PP. 60-62 2017.
[12] Zhan Yongjie, Wang Jian, Shi Jianping, Cheng Guangliang, Yao Lele, And Sun Weidong. “Distinguishing Cloud And Snow In Satellite Images Via Deep Convolutional Network”. In IEEE Geoscience And Remote Sensing Letters, 1545-598x, pp.3-4 2017.
[13] Persello Claudio.” Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images”. In IEEE geoscience and remote sensing letters, vol. 14, no. 12, pp 2327-2328, december 2017.
[14] Siti Nor Khuzaimah Binti Amit, Shiraishi Soma, Inoshita Tetsuo, Aoki Yoshimitsu. “Analysis Of Satellite Images For Disaster Detection”. In IEEE, 978-1-5090-3332-4/16/$31.00, pp. 5190-5191, 2016.
[15] Zeng Haoyue, Xia Yong. “Space Target Recognition based on Deep Learning”. In 20th International Conference on Information Fusion Xi`an, China - July 10-13, 2017.