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This paper is to study the principle of line spectrum feature extraction. In view of the deficiency of manual line spectrum feature extraction method, a line spectrum feature extraction method based on machine learning was proposed. The Encoder-Decoder based on convolution neural network was built, and the attention mechanism was introduced between the convolution and pooling layers, so that the important features of the input data could occupy a higher weight to enhance the accuracy of feature extraction. The model was compared with U-Net model and TPSW algorithm in the case of low signal-to-noise ratio, and tested on the actual data. The experimental results show that the improved model achieves a line positioning accuracy of 0.823 at a signal-to-noise ratio of 5 dB. This performance is better than that of the U-Net model and TPSW algorithm with in the 0~5 dB range. Thus the model effectively extracts line spectrum information and improves the accuracy of underwater target detection.
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| 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 |
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