Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study

摘要: A Hyperspectral Image(HSI) contains much more number of channels as compared to a Red,Green,Blue(RGB) image, hence containing more information about entities within the image. The Convolutional Neural Network (CNN) and the Multi-Layer Perceptron (MLP) have been proven to be an effective method of image classification. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data, to achieve the expected outcome. These issues become more complex while dealing with hyperspectral images. To decrease the training time and reduce the dependence on large labeled dataset, method of transfer learning is proposed in this paper. The hyperspectral dataset is preprocessed to a lower dimension using Principal Component Analysis(PCA), then Deep Learning(DL) models are applied to it for the purpose of classification. The features learned by this model …