A Fast 3D CNN for Hyperspectral Image Classification

Abstract

Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial information, therefore, 3D CNN can be an alternative but highly computational complex due to the volume and spectral dimensions. Furthermore, these models do not extract quality feature maps and may underperform over the regions having similar textures. Therefore, this work proposed a 3D CNN model that utilizes both spatial-spectral feature maps to attain good performance. In order to achieve the said performance, the HSI cube is first divided into small overlapping 3D patches. Later these patches are processed to generate 3D feature maps using a 3D kernel function over multiple contiguous bands that persevere the spectral information as well. Benchmark HSI datasets (Pavia University, Salinas and Indian Pines) are considered to validate the performance of our proposed method. The results are further compared with several state-of-the-art methods.

5 pages, 8 figures, (IEEE GRSL)

References in corpus (3)

• HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
• Three dimensional Deep Learning approach for remote sensing image classification
• HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

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• Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification
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• Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
• Spectral Image Data Fusion for Multisource Data Augmentation
• 3D/2D regularized CNN feature hierarchy for Hyperspectral image classification
• Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
• IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications