.png){kind=icon}
返回
Efficient Classification of Hyperspectral Data Using Deep Neural Network Model
.png){kind=icon}
PRE
AI
DOI：10.22967/HCIS.2022.12.035.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, there has been tremendous progress in the classification of hyperspectral images (HSI) using a deep neural network. Due to the high complexity of spectral properties, limited ground truth samples, and extreme inhomogeneity of hyperspectral data, an efficient classification of HSI using deep convolutional neural networks remains difficult. As a result, the ever-increasing volume of data necessitates the effective categorization of remote sensor-based HSI utilizing advanced deep learning. Over time, certain deep learning models for learning to detect HSI have been developed, and many researchers have used convolutional neural networks (CNNs). Previous research on hyperspectral data categorization using deep learning models have faced the issue of performance degradation due to insufficient layer selection in the CNN, which is studied in this research. In our solution, we have proposed a CNN model for effectively classifying hyperspectral data using deep learning. We tested our method using land cover benchmark datasets and found that it outperforms the current state-of-the-art. When compared to conventional machine learning methods and baseline procedures, the results of this study reveal that using an upgraded CNN model significantly improves the performance (accuracy = 93.621, precision = 91.571, recall = 92, F1 score = 90.714).
Keyword:
Hyperspectral Images
Deep Learning
Remote Sensor
Convolutional Neural Networks
期刊
IF:
3
论文数:
555
被引数:
1.4K
机构
引用论文
暂无论文信息