Hyperspectral imaging is the most popular monitoring tool of the Earth’s surface. From the SE-AB-DenseNet with the cutout model, the overall accuracies for the Indian Pines and Salinas datasets were observed to be 99.37 and 99.78, respectively. The experiment results on the datasets show a competitive classification accuracy when compared with state-of-the-art methods with limited training samples. The experiments were carried out on two commonly used hyperspectral datasets (Indian Pines and Salinas).

Additionally, the cutout regularization technique is used for HSI spatial–spectral classification to overcome the problem of overfitting. The AdaBound optimizer gives the proposed model the ability to improve its stability and enhance its classification accuracy by approximately 2%.

The dense network is combined with the AdaBound and squeeze–excitation modules to give lower computation costs and better classification performance. In this paper, an optimized squeeze–excitation AdaBound dense network (SE-AB-DenseNet) is designed to emphasize the significant spatial–spectral features of HSI data. Usually, CNN has a large number of trainable parameters, which increases the computational complexity of HSI data. Further, the network architecture becomes more complex when different spatial–spectral feature information is extracted. However, the complexity in classifying the HSI data using a common convolutional neural network is still a challenge. Deep learning approaches have made tremendous progress in hyperspectral image (HSI) classification. Increasing importance in the field of artificial intelligence has led to huge progress in remote sensing.

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