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Hybrid 3DNet: Hyperspectral Image Classification with Spectral-spatial Dimension Reduction using 3D CNN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 23 |
| Year of Publication: 2022 |
| Authors: Zareen Binta Zakaria, Md. Rashedul Islam |
10.5120/ijca2022922270
|
Zareen Binta Zakaria, Md. Rashedul Islam . Hybrid 3DNet: Hyperspectral Image Classification with Spectral-spatial Dimension Reduction using 3D CNN. International Journal of Computer Applications. 184, 23 ( Jul 2022), 6-11. DOI=10.5120/ijca2022922270
Abstract
Hyperspectral image classification (HSI) is a fantastic approach for assessing diverse land cover utilizing remotely sensed hyperspectral images and has been an established research topic. The term classification is used in remote sensing to refer to the process of assigning individual pixels to a group of classes. The utilization of CNN for HSI classification is likewise noticeable in ongoing works. These approaches are generally founded on 2-D CNN. For practical purposes, a 2D Convolutional Neural Network (CNN) is a viable option; however, these models do not provide high-quality feature maps because a 3D data cube, a Hyperspectral image,contains both two-dimensional spatial information (image feature) and one-dimensional spectral information (spectral-bands). Therefore, 3D CNN can be another option, yet it has high computational complexity because of the volume and spectral dimensions. This paper proposed a 3D CNN model that achieves excellent results by combining spatial and spectral feature maps. The performance of our proposed method is approved using three standard HSI datasets (Pavia University, Indian Pines, and Salinas), and the outcomes are further compared with several state-of-the-art methods.
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