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Fusion Based Feature Extraction and Optimal Feature Selection in Remote Sensing Image Retrieval

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Abstract

In remote sensing (RS) community, RSIR (Remote Sensing Image Retrieval) is considered as a tough topic and gained more attention because the data is collected via EO (Earth Observation) satellites. As huge numbers of RS images are available, the lack of labelled samples, complex contents obstructs the understanding of RS images. Therefore, accurate and effective image retrieval (IR) system named fusion based feature extraction and meta-heuristic algorithm based feature selection is presented in this work for performing RSIR. Pre-processing is done using Kernel PCA (KPCA). Next, fusion of 3 CNN (Fused CNN) architectures namely Visual Geometry Group (VGG 16, VGG 19) and ResNet (Residual Network) is used for feature extraction. The selection of features is performed using Joint MI (Joint Mutual Information) optimized using RFO (Rain-Fall Optimization) algorithm. Next, similarity is measured using Weighted Euclidean Distance (WED) metric. Finally, Relevance Feedback Model (RFM) verifies whether the search results have met the user query. The implementation tool is PYTHON and the three online databases used for testing are WHU-RS19, AID, and UCM. Hence, the simulation outcomes reveal that the presented Fused CNN model achieved improved mAP performances such as 93.693%, 94.716%, and 95.067% on the datasets than the baseline architectures.

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References

  1. Alberton B, Torres RS, Cancian FL, Borges BD, Almeida J, Mariano GC, dos Santos J, Morellato LPC (2017) Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspectives Ecol Conserv 15(2):82–90

    Article  Google Scholar 

  2. Avtar R, Kumar P, Oono A, Saraswat C, Dorji S, Hlaing Z (2017) Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas. Geocarto Int 32(8):874–885

    Article  Google Scholar 

  3. Boualleg Y, Farah M, and Farah IR (2020) TLDCNN: A Triplet Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval. In 2020 Mediterranean and middle-east geoscience and remote sensing symposium (M2GARSS), 13–16. IEEE.

  4. Cao R, Zhang Q, Zhu J, Li Q, Li Q, Liu B, Qiu G (2020) Enhancing remote sensing image retrieval using a triplet deep metric learning network. Int J Remote Sens 41(2):740–751

    Article  Google Scholar 

  5. Chaudhuri U, Banerjee B, Bhattacharya A, Datcu M (2020) Cmir-net: a deep learning based model for cross-modal retrieval in remote sensing. Pattern Recogn Lett 131:456–462

    Article  Google Scholar 

  6. Deng Z, Sun H, Zhou S (2018) Semi-supervised ground-to-aerial adaptation with heterogeneous features learning for scene classification. ISPRS Int J Geo Inf 7(5):182

    Article  Google Scholar 

  7. El Mahrad B, Newton A, Icely JD, Kacimi I, Abalansa S, Snoussi M (2020) Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: a review. Remote Sens 12(14):2313

    Article  Google Scholar 

  8. Gaikwad VP and Musande V (2017) Wheat disease detection using image processing. In 2017 1st international conference on intelligent systems and information management (ICISIM), IEEE 110-112.

  9. Imbriaco R, Sebastian C, Bondarev E (2019) Aggregated deep local features for remote sensing image retrieval. Remote Sens 11(5):493

    Article  Google Scholar 

  10. Johnson BA, Iizuka K, Bragais MA, Endo I, Damasa B (2017) Magcale-Macandog. Employing crowdsourced geographic data and multi-temporal/multi-sensor satellite imagery to monitor land cover change: a case study in an urbanizing region of the Philippines. Computers. Environ Urban Syst 64:184–193

    Article  Google Scholar 

  11. Kashif M, Raja G, Shaukat F (2020) An efficient content-based image retrieval system for the diagnosis of lung diseases. J Digit Imaging 33:971–987

    Article  Google Scholar 

  12. Kharat SA, Musande VB (2015) Cotton crop discrimination using landsat-8 data. Int J Comp Sci Inform Technol 6(5):4381–4384

    Google Scholar 

  13. Lang S, Hay GJ, Baraldi A, Tiede D, Blaschke T (2019) GEOBIA achievements and spatial opportunities in the era of big earth observation data. ISPRS Int J Geo Inf 8(11):474

    Article  Google Scholar 

  14. Langat PK, Kumar L, Koech R (2019) Monitoring river channel dynamics using remote sensing and GIS techniques. Geomorphology 325:92–102

    Article  Google Scholar 

  15. Lausch A, Erasmi S, King DJ, Magdon P, Heurich M (2017) Understanding forest health with remote sensing-part II—A review of approaches and data models. Remote Sens 9(2):129

    Article  Google Scholar 

  16. Leonardo MM, Carvalho TJ, Rezende E, Zucchi R, and Faria FA (2018) Deep feature-based classifiers for fruit fly identification (diptera: Tephritidae). In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), 41–47. IEEE.

  17. Li Y, Zhang H, Xue X, Jiang Y, Shen Q (2018) Deep learning for remote sensing image classification: a survey. Wiley Interdisciplinary Rev: Data Mining Knowledge Discovery 8(6):1264

    Google Scholar 

  18. Liu Y, Ding L, Chen C, Liu Y (2020) Similarity-based unsupervised deep transfer learning for remote sensing image retrieval. IEEE Trans Geosci Remote Sens

  19. Liu P, Gou G, Shan X, Tao D, Zhou Q (2020) Global optimal structured embedding learning for remote sensing image retrieval. Sensors 20(1):291

    Article  Google Scholar 

  20. Ma C, Chen F, Yang J, Liu J, Xia W, Li X (2018) A remote-sensing image-retrieval model based on an ensemble neural networks. Big Earth Data 2(4):351–367

    Article  Google Scholar 

  21. Musande V, Kumar A, Roy PS, Kale K (2013) Evaluation of fuzzy-based classifiers for cotton crop identification. Geocarto Int 28(3):243–257

    Article  Google Scholar 

  22. Napoletano P (2018) Visual descriptors for content-based retrieval of remote-sensing images. Int J Remote Sens 39(5):1343–1376

    Article  Google Scholar 

  23. Sadeghi-Tehran P, Angelov P, Virlet N, Hawkesford MJ (2019) Scalable database indexing and fast image retrieval based on deep learning and hierarchically nested structure applied to remote sensing and plant biology. J Imaging 5(3):33

    Article  Google Scholar 

  24. Shao Z, Zhou W, Deng X, Zhang M, Cheng Q (2020) Multilabel remote sensing image retrieval based on fully convolutional network. IEEE J Selected Topics Appl Earth Observ Remote Sensing 13:318–328

    Article  Google Scholar 

  25. Sudmanns M, Tiede D, Lang S, Bergstedt H, Trost G, Augustin H, Baraldi A, Blaschke T (2020) Big earth data: disruptive changes in earth observation data management and analysis? Inter J Digital Earth 13(7):832–850

    Article  Google Scholar 

  26. Tahmasebi P, Kamrava S, Bai T, Sahimi M (2020) Machine learning in geo-and environmental sciences: from small to large scale. Adv Water Resour 103619:103619

    Article  Google Scholar 

  27. Temitope YS, Balogun AL (2020) Advances in remote sensing technology, machine learning and deep learning for marine oil spill detection, prediction and vulnerability assessment. Remote Sens 12(20):3416

    Article  Google Scholar 

  28. Tiede D, Baraldi A, Sudmanns M, Belgiu M, Lang S (2017) Architecture and prototypical implementation of a semantic querying system for big earth observation image bases. Eur J Remote Sensing 50(1):452–463

    Article  Google Scholar 

  29. Vharkate MN, Musande VB (2021) Remote sensing image retrieval using hybrid visual geometry group network with relevance feedback. Int J Remote Sens 42(14):5540–5567

    Article  Google Scholar 

  30. Wang Y, Ji S, Lu M, Zhang Y (2020) Attention boosted bilinear pooling for remote sensing image retrieval. Int J Remote Sens 41(7):2704–2724

    Article  Google Scholar 

  31. Xia GS, Tong XY, Hu F, Zhong Y, Datcu M, and Zhang L (2017) Exploiting deep features for remote sensing image retrieval: a systematic investigation. arXiv preprint arXiv:1707.07321 2.

  32. Xiong W, Xiong Z, Cui Y, Lv Y (2020) A discriminative distillation network for cross-source remote sensing image retrieval. IEEE J Selected Topics Appl Earth Observ Remote Sensing 13:1234–1247

    Article  Google Scholar 

  33. Yang C, Yu M, Li Y, Hu F, Jiang Y, Liu Q, Sha D, Xu M, Gu J (2019) Big earth data analytics: a survey. Big Earth Data 3(2):83–107

    Article  Google Scholar 

  34. Zhou W, Newsam S, Li C, Shao Z (2018) PatternNet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS J Photogramm Remote Sens 145:197–209

    Article  Google Scholar 

  35. Zhuo Z, Zhou Z (2021) Remote sensing image retrieval with Gabor-CA-ResNet and Split-based deep feature transform network. Remote Sens 13(5):869

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. B.A.M.U Aurangabad, Maharashtra, India

    Minakshi N. Vharkate

  2. Department of Computer Engineering, MIT Academy of Engineering, Alandi(d), Pune, Maharashtra, India

    Minakshi N. Vharkate

  3. Department of Computer Science and Engineering, Jawaharlal Nehru Engineering College, Aurangabad MGM University, Aurangabad MGM University, Maharashtra, Maharashtra, India

    Vijaya B. Musande

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  1. Minakshi N. Vharkate
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Correspondence to Minakshi N. Vharkate.

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Authors Minakshi N Vharkate and Dr. Vijaya B. Musande declares that they have no conflict of interest.

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Vharkate, M.N., Musande, V.B. Fusion Based Feature Extraction and Optimal Feature Selection in Remote Sensing Image Retrieval. Multimed Tools Appl 81, 31787–31814 (2022). https://doi.org/10.1007/s11042-022-11997-y

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  • DOI: https://doi.org/10.1007/s11042-022-11997-y

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