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• W4306154313 endingPage "737" @default.
• W4306154313 startingPage "721" @default.
• W4306154313 abstract "As image sensor electronics have progressed, and hyperspectral images have been used in a wide range of applications. In terms of recognizing the classes, a lot of research work has been done to extract useful information from the available unstructured knowledge database. The use of spectral and geographical datatypes in images can improve the classification precision. To improve the accuracy of the energetic-spectral snap analysis, combining dimensional and spectral data is a good idea. This research study examines the history of dimensional facts based on energetic-spectral image classification designs by using prepared and semi-directed classifiers to classify detached sensing images with particularized class labels. Long-term data are removed if the features are protensive. To increase the veracity of classification, the extracted traits are prepared by utilizing multiple classifiers. The preparation and sinking balance towards the loss function have been reused to train the classifiers. To avoid local minima, the preparation is approved by utilizing various tiers for accompanying the extra balancing limits. To overcome the risk of establishing a contradictory validity image, each detached perceiving image is classified throughout the experiment stage. Exploratory discoveries demand higher validity in-class criteria than other advanced directed classifier techniques." @default.
• W4306154313 created "2022-10-14" @default.
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• W4306154313 date "2022-10-14" @default.
• W4306154313 modified "2023-10-01" @default.
• W4306154313 title "Study of Land Cover Classification from Hyperspectral Images Using Deep Learning Algorithm" @default.
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• W4306154313 doi "https://doi.org/10.1007/978-981-19-3035-5_54" @default.
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