SHBO-based U-Net for image segmentation and FSHBO-enabled DBN for classification using hyperspectral image

References

• Paoletti ME, Haut JM, Pereira NS, et al. Ghostnet for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2021;59(12):10378–10393.
   Web of Science ®Google Scholar
• Yang R, Su L, Zhao X, et al. Representative band selection for hyperspectral image classification. J Vis Commun Image Represent. 2017;48:396–403.
   Web of Science ®Google Scholar
• Liang X, Zhang Y, Zhang J. Attention symbiotic neural network for hyperspectral image refined classification based on relative water content retrieval. IEEE Trans Geosci Remote Sens. 2020;59(7):5998–6016.
   Web of Science ®Google Scholar
• Liu R, Cai W, Li G, et al. Hybrid dilated convolution guided feature filtering and enhancement strategy for hyperspectral image classification. IEEE Geosci Remote Sens Lett. 2021;19:1–5.
   Web of Science ®Google Scholar
• Cai W, Wei Z, Liu R, et al. Remote sensing image recognition based on multi-attention residual fusion networks. ASP Trans Pattern Recognit Intell Syst. 2021;1(1):1–8.
   Google Scholar
• Jegatheeswari P, Angelin Deepa T. Fuzzy weighted least square filter for pansharpening in satellite images. Multimed Res. 2019;2(1):17–22.
   Google Scholar
• Serranti S, Palmieri R, Bonifazi G, et al. Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging. Waste Manage. 2018;76:117–125.
   PubMed Web of Science ®Google Scholar
• Wang M, Wang Q, Hong D, et al. Learning tensor low-rank representation for hyperspectral anomaly detection. IEEE Trans Cybern. 2022;53:679–691.
   PubMed Web of Science ®Google Scholar
• Dumke I, Ludvigsen M, Ellefmo SL, et al. Underwater hyperspectral imaging using a stationary platform in the trans-Atlantic geotraverse hydrothermal field. IEEE Trans Geosci Remote Sens. 2018;57(5):2947–2962.
   Web of Science ®Google Scholar
• Chang CI, Kuo YM, Chen S, et al. Self-mutual information-based band selection for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2020;59(7):5979–5997.
   Web of Science ®Google Scholar
• Hong D, Yokoya N, Chanussot J, et al. An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Trans Image Process. 2019;28(4):1923–1938.
   Web of Science ®Google Scholar
• Ye A, Zhou X, Miao F. Innovative hyperspectral image classification approach using optimized CNN and ELM. Electronics (Basel). 2022;11(5):775.
   Web of Science ®Google Scholar
• Cai W, Liu B, Wei Z, et al. TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimed Tools Appl. 2021;80(7):11291–11312.
   Web of Science ®Google Scholar
• Thangam T. Adaptive filter using improved pigeon inspired optimization algorithm for satellite image denoising. Multimed Res. 2020;3(3):29–35.
   Google Scholar
• Xue Z, Yu X, Liu B, et al. HResNetAM: hierarchical residual network with attention mechanism for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:3566–3580.
   Google Scholar
• Chen Y, Jiang H, Li C, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens. 2016;54(10):6232–6251.
   Web of Science ®Google Scholar
• Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015, p. 1–9.
   Google Scholar
• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016; p. 770–778.
   Google Scholar
• Hashim FA, Hussien AG. Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl Based Syst. 2022;242:108320.
   Web of Science ®Google Scholar
• Hashim FA, Houssein EH, Hussain K, et al. Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul. 2022;192:84–110.
   Web of Science ®Google Scholar
• Mou L, Saha S, Hua Y, et al. Deep reinforcement learning for band selection in hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2021;60:1–14.
   Web of Science ®Google Scholar
• Shah C, Du Q, Xu Y. Enhanced TabNet: attentive interpretable tabular learning for hyperspectral image classification. Remote Sens (Basel). 2022;14(3):716.
   Web of Science ®Google Scholar
• Fang B, Liu Y, Zhang H, et al. Hyperspectral image classification based on 3D asymmetric inception network with data fusion transfer learning. Remote Sens (Basel). 2022;14(7):1711.
   Web of Science ®Google Scholar
• Hong D, Gao L, Yokoya N, et al. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Trans Geosci Remote Sens. 2021;59(5):4340–4354.
   Web of Science ®Google Scholar
• Hong D, Gao L, Yao J, et al. Graph convolutional networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2021;59(7):5966–5978.
   Web of Science ®Google Scholar
• Yao J, Cao X, Hong D, et al. Semi-active convolutional neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2022;60.
   Google Scholar
• Hyperspectral Remote Sensing Scenes data sets will be acquired from: http://www.ehu.eus/ccwintco/index.php?title = Hyperspectral_Remote_Sensing_Scenes, accessed on May 2022.
   Google Scholar
• Chakraborti T, McCane B, Mills S, et al. LOOP descriptor: local optimal-oriented pattern. IEEE Signal Process Lett. 2018;25(5):635–639.
   Web of Science ®Google Scholar
• Bhaladhare PR, Jinwala DC. A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Adv Comput Eng. 2014: 1–12.
   Google Scholar
• Kumar A, Sodhi SS. Comparative analysis of Gaussian filter, median filter and denoise autoenocoder. In: Proceedings of 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom); 2020; p. 45–51.
   Google Scholar
• Luo K, Qin Y, Yin D, et al. Hyperspectral image classification based on pre-post combination process. In: 6th International Conference on Systems and Informatics (ICSAI); 2019 Nov; p. 1275–1279.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. ., U-net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention; 2015; p. 234–241.
   Google Scholar
• Xie S, Shan S, Chen X, et al. Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans Image Process. 2010;19(5):1349–1361.
   PubMed Web of Science ®Google Scholar
• Jayachandran A, Dhanasekaran R. Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine. Int J Imaging Syst Technol. 2013;23(2):97–103.
   Web of Science ®Google Scholar
• Ramesh PS, Letitia S. Parallel architecture for cotton crop classification using WLI-fuzzy clustering algorithm and Bs-lion neural network model. Imaging Sci J. 2017;65(8):438–456.
   Web of Science ®Google Scholar
• Vojt J. Deep neural networks and their implementation; 2016.
   Google Scholar
• Bilgin G, Erturk S, Yildirim T. Segmentation of hyperspectral images via subtractive clustering and cluster validation using one-class support vector machines. IEEE Trans Geosci Remote Sens. 2011;49(8):2936–2944.
   Web of Science ®Google Scholar
• Nalepa J, Myller M, Imai Y, et al. Unsupervised segmentation of hyperspectral images using 3-D convolutional autoencoders. IEEE Geosci Remote Sens Lett. 2020;17(11):1948–1952.
   Web of Science ®Google Scholar
• Eches O, Benediktsson JA, Dobigeon N, et al. Adaptive markov random fields for joint unmixing and segmentation of hyperspectral images. IEEE Trans Image Process. 2012;22(1):5–16.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Liu K, Dong Y, et al. Semisupervised classification based on SLIC segmentation for hyperspectral image. IEEE Geosci Remote Sens Lett. 2019;17(8):1440–1444.
   Web of Science ®Google Scholar
• Dalal A-A, Cai Z, Al-qaness MAA, et al. ETR: enhancing transformation reduction for reducing dimensionality and classification complexity in hyperspectral images. Expert Syst Appl. 2023;213.
   Web of Science ®Google Scholar
• Dalal A-A, Al-qaness MAA, Cai Z, et al. IDA: improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images. Pattern Recognit. 2023;134:1–18.
   Web of Science ®Google Scholar
• Dalal A-A, Cai Z, Al-qaness MAA, et al. Compression and reinforce variation with convolutional neural networks for hyperspectral image classification. Appl Soft Comput. 2022;130.
   PubMed Web of Science ®Google Scholar
• Dalal A-A, Al-qaness MAA, Cai Z, et al. Meta-learner hybrid models to classify hyperspectral images. Remote Sens (Basel). 2022;14(4.
   Web of Science ®Google Scholar