Deep learning with convolutional neural networks (CNNs) is very powerful for image analysis. Of late, deep architectures in machine learning and artificial intelligence (AI) have attracted industry and academia significantly. CNNs could produce impressive results across diversified set of problems in different fields. From the literature, CNNs are found to be ideal candidates for learning of data that is represented in images. Similarly, they are used to exploit the content of Synthetic Aperture Radar (SAR) images with their fundamental building blocks known as convolutions. In this paper, CNN based architecture is implemented for SAR image classification. Multiple convolutional layers are used to form a deep neural network and extract features at different levels. The novelty of the research is to have efficient multi-labelling. Multiple convolutional layers are used to form a deep neural network and extract features at different levels. Unlike many conventional machine learning algorithms, the proposed method learns multiple discriminative features from the training set without the need for prior specification from human expert. This property is efficiently used with a classifier that can learn from previously unknown trends. Besides, it finds latent dependencies between labels and use this knowhow while performing multi-label classification. Thus it can improve performance of SAR image retrieval as part of Content Based Image Retrieval (CBIR) system. SAR data is collected from Amazon Basin dataset which is available to public. Performance of deep learning models such as ResNet-50, VGG-16 and VGG-19 are evaluated. ResNet-50 showed better performance over VGG-16 and VGG-19. Unlike many conventional machine learning algorithms, the proposed method learns multiple discriminative features from the training set without the need for prior specification from human expert. This property is efficiently used with a classifier that can learn from previously unknown trends. Besides, it finds latent dependencies between labels and use this knowhow while performing multi-label classification. Thus it can improve performance of SAR image retrieval as part of CBIR system. With deep learning, the proposed architecture is found to be efficient than many competitive methods found in the literature.
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Cabral R, Torre FD, la Costeira JP, Bernardino A (2015) Matrix completion for weakly-supervised multi-label image classification. IEEE Trans Pattern Anal Mach Intell 37(1):121–135
Chen T, Wang Z, Li G, Lin L (2018) Recurrent attentional reinforcement learning for multi-label image recognition. Assoc Adv Artif Intell. 32:6730–6737
Devkar RV, Shiravale S (2017) Relevant label identification for multi-label image classification. IJEDR 5(3):337–343
Duan G, Yang J, Yang Y (2011) Content-based image retrieval research. Phys Procedia 22:471–477
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Jabreel M, Moreno A (2019) A deep learning-based approach for multi-label emotion classification in tweets. Appl Sci 9(6):1–16
Jenitta A, Ravindran RS (2018) Content based geographic image retrieval using local vector pattern. Braz Arch Biol Technol 61:P1-10
Jing L, Yang L, Yu J, Ng MK (2015) Semi-supervised low-rank mapping learning for multi-label classification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1483–1491
Karalas K, Tsagkatakis G, Zervakis M, Tsakalides P (2015) Deep learning for multi-label land cover classification. School Electr Comput Eng 9643:1–14
Kumar N, Rathee M, Chandran N, Gupta D, Rastogi A, Sharma R (2020) CrypTFlow: secure tensorflow inference. IEEE Sympos Secur Priv (SP) 2020:P1-18
Li J, Narayanan RM (2013) Integrated information mining and image retrieval in remote sensing. Department of Computer Science and Information Technology, pp 1–21
Li X, Wang L, Sang E (2004) Multi-label SVM active learning for image classification. In: International conference on image processing, 2004. ICIP′04, pp 5227–5231
Liu Y, Sheng L, Shao J, Yan J, Xiang S, Pan C (2018) Multi-label image classification via knowledge distillation from weakly-supervised detection. In: 2018 ACM multimedia conference on multimedia conference—MM, pp 1–10
Luo Y, Tao D, Xu C, Xu C, Liu H, Wen Y (2013a) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709–722
Luo Y, Tao D, Xu C, Li D, Xu C (2013b) Vector-valued multi-view semi-supervised learning for multi-label image classification. Assoc Adv Artif Intell 27:647–653
Mane PP, Bawane NG (2016) An effective technique for the content based image retrieval to reduce the semantic gap based on an optimal classifier technique. Pattern Recognit Image Anal 26(3):597–607
Nhu V-H, Hoang N-D, Nguyen H, Ngo PTT, Thanh Bui T, Hoa PV, Tien BD (2020) Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. CATENA 188:P1-13
Planet (2021) Understanding the Amazon from space. https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data.
Read J, Perez-Cruz F (2014) Deep learning for multi-label classification. pp 1–8
Rostami M, Kolouri S, Eaton E, Kim K (2019) Deep transfer learning for few-shot sar image classification. Remote Sens 11(11):1374
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Preprint at arXiv: 1409.1556
Singh AV (2015) Content-based image retrieval using deep learning. Rochester Institute of Technology, pp 1–44
Sowmya Rani RN, Reddy S (2012) Comparative study on content based image retrieval. Int J Fut Comput Commun 1(4):366–368
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016). Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Vetrithangam D, UmaMaheswari N, Venkatesh R (2016) Dynamic content-based image search and retrieval by combining low level features. Int J Adv Eng Technol 7(2):898–906
Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval. In: Proceedings of the ACM international conference on multimedia—MM 14, pp 157–166
Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W (2016) CNN-RNN: a unified framework for multi-label image classification. IEEE Conf Comput vis Pattern Recognit (CVPR) 2016:P2285-2294
Wang Z, Chen T, Li G, Xu R, Lin L (2017) Multi-label image recognition by recurrently discovering attentional regions. IEEE, pp 464–472
Wang C, Tandeo P, Mouche A, Stopa JE, Gressani V, Longepe N, Chapron B (2019) Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies. Remote Sens Environ 234:111457
Wei Y, Xia W, Lin M, Huang J, Ni B, Dong J, Yan S (2016) HCP: a flexible CNN framework for multi-label image classification. IEEE Trans Pattern Anal Mach Intell 38(9):1901–1907
Xu L, Wang Z, Shen Z, Wang Y, Chen E (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: 2014 IEEE international conference on data mining, pp 1067–1072
Zhu F, Li H, Ouyang W, Yu N, Wang X. (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. IEEE, pp 5513–5522
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krishna, G.S., Prakash, N. Deep learning for efficient and multi-labelled classification of synthetic aperture radar images. Evolving Systems 12, 741–754 (2021). https://doi.org/10.1007/s12530-021-09390-5
- Deep learning
- Convolutional neural network (CNN)
- SAR image classification
- Multi-feature extraction
- Multi-label classification