Hyperspectral Image Classification using Hybrid Deep Convolutional Neural Network

Abstract

The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

“…Bidari et al also employed a deep learning algorithm to classify hyperspectral images [34]. In addition, more recently, many researchers have proposed deep learning, including some dimension reduction to improve the efficiency of the hyperspectral-image-classification model [31,[35][36][37][38][39][40]. Liu et al applied a deep learning approach to classify and reconstruct hyperspectral images using MDL40w and achieved a 94.00% performance [41].…”