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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Abstract

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset.

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Data Availability

The data used for experimentation is available publicly.

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology-Deemed-to-be-University, Bhubaneswar, India

    Anasua Banerjee, Minakhi Rout & Mainak Bandyopadhyay

  2. Department of Computer Science and Engineering, National Institute of Technology, Silchar, India

    Satyajit Swain

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  1. Anasua Banerjee
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  2. Satyajit Swain
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Correspondence to Mainak Bandyopadhyay.

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Banerjee, A., Swain, S., Rout, M. et al. Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19327-0

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