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• W4383603018 abstract "Hyper spectral image (HI) classification models have been contributing in a wide spectrum of real-world applications ranging from human-need based applications to global safety issues with core concepts of classification, segmentation, anomaly detection and prediction. The use of DL on satellite images and to achieve best performance, the research is swiftly trending from traditional machine learning to deep learning approaches. In this chapter, first we discuss the narratives of HI, source of satellite images and the role of deep learning based classification on sentinel-2 satellite HI data using 3D-CNN and validated the ground truth. In the latter part, we propose a hybrid multi-scale-spinal-net-DL technique (Hybrid MSSN) that can address some of key issues. The ensemble model comprises with multi-scale CNN and spinal fully connected network (SFCN) to classify the Hyperspectral satellite image. Along with that, we demonstrate how the combination of 3D-CNNs and 2D-CNN play its significance in extracting the characteristics of spectral and spatial features of HI, in addition to the Principal Component Analysis (PCA) based spectral band isolations [15]. The role of SFCN or spinal net in our model to reduce the process of global mapping with an aim for fast response and reduced computing effort in the activation function of NNs. Investigations on three common datasets showed considerable classification accuracy in comparison to four related models, despite the fact that there were few training samples, noise, and class imbalance concerns. Experimental outcomes through Python libraries in Google-Colab platform demonstrate that in comparison with other discussed models, our models consistently achieve better accuracy in all classes with 99.16% in case of 30% training without oversampling and 99.99% in case of with oversampling." @default.
• W4383603018 created "2023-07-08" @default.
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• W4383603018 date "2023-01-01" @default.
• W4383603018 modified "2023-10-18" @default.
• W4383603018 title "Hyperspectral Images: A Succinct Analytical Deep Learning Study" @default.
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• W4383603018 doi "https://doi.org/10.1007/978-981-99-3784-4_8" @default.
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