Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. , a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation,  test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.
['Ying Li', 'Haokui Zhang', 'Qiang Shen']
I am writing a research paper on a idea Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
[{"from": "human", "value": "I am writing a research paper on a idea Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent research has shown that using spectral\u2013spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral\u2013spatial features within such images. , a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral\u2013spatial-combined features effectively. In addition, it requires fer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation,  test the proposed method along with three other deep learning-based HSI classification methods\u2014namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods\u2014on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record."}]