PDF
Full
Outline
Hyperspectral target detection based on deep learning faces challenges such as insufficient quality of samples, intricate network structures, and laborious parameter adjustment. In this paper, we propose a deep learning method with data augmentation and automatic hyperparameter optimization. To tackle the issue of insufficient quality of samples, we introduce a sample augmentation strategy. The strategy utilizes endmember extraction and clustering techniques to directly acquire a large number of background pixels from hyperspectral images. By pairing these with a small number of known target pixels using a phase-reducing pixel pairing approach, we obtain a large number of labeled pure sample pairs, thereby accomplishing data augmentation. In addition, distinct from most complex deep networks, we designed a lightweight Convolutional Neural Network (CNN) comprised of 12 convolutional layers. This network is specifically engineered to efficiently and rapidly learn the mapping between input sample pairs and their corresponding labels. By incorporating the particle swarm optimization algorithm, this network possesses the capability to automatically optimize hyperparameters, overcoming the shortcomings of laborious parameter adjustment. This enables the network to automatically adjust hyperparameters based on samples from different hyperspectral images, thereby generating optimal results. For a test pixel, the input to the trained network is the spectral difference between the central pixel and its adjacent pixels. When a test pixel belongs to the target, the output score is closely align with the target label. Experimental results on five hyperspectral datasets demonstrate that our method significantly outperforms existing techniques.
PDF
94
42
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
