Predictive analysis on hyperspectral images using deep learning

• Published in: Journal of physics. Conference series 2020-12, Vol.1716 (1), p.12058
• Main Authors: Sarah Rajkumar, E, Pattabiraman, V
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Convolution neural network classifier; Deep learning; Feature extraction; Hyperspectral imaging; Image classification; Machine learning; Physics; Prediction; Remote sensing; Spectral domain
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1716/1/012058

In the field of remote sensing, identification of hyperspectral images (HSI) has become a trending topic. Hyperspectral visualization also struggles with a non-linear relationship between the spectral data obtained and the actual material in the image. In the recent years new machine learning algorithms have been established as an efficient feature extraction method to tackle non-linear hitches efficiently and commonly cast-off in a variety of image classification tasks. In addition, deep learning was applied to identify the features of the HSI and demonstrated good performance inspired by various positive applications. This research paper provides us with an ordered review of deep learning Hyperspectral image classification literature available and it compares multiple propositions. Exclusively, we summarize the main HSI classification challenges that conventional machine learning approaches have not been able to solve successfully, and also incorporate the benefits of deep learning to address these issues. This study improved data abstraction with minimized uncertainty and enhanced HSI classification performance. Firstly, a CNN model is built to understand the HSI"s spectral functionality. The CNN is used as a pixel classifier; therefore, it operates only in the spectral domain.