Underwater target recognition plays a very important role in the process of ocean exploration.Hyperspectral image(HSI)provides rich spectral spatial information by stacking hundreds of continuous bands.How to accurately extract target information from rich hyperspectral underwater information has become a challenge.This is usually solved by using convolutional neural network(CNN)with a fixed size receptive field(RF).However,when using forward and back propagation to optimize the network,these solutions fail to enable neurons to efficiently adjust receptive field sizes and cross-channel dependencies.In this paper,a hyperspectral underwater target classification and recognition algorithm for UAV based on spatial-spectral residual network is proposed.The network has spectral attention,realizes adaptive receptive field,and can capture discriminative spectral spatial features for human-computer interaction classification in an end-to-end training manner.Firstly,the SG(Savitzky-Golay)smoothing process is used to eliminate the high-frequency jitter of the spectral curve caused by noise,retain the effective peak and valley morphology of the spectral curve,and improve the accuracy of subsequent hyperspectral processing.Then,the principal components analysis(PCA)method is used to reduce the dimension of the denoised spectral image and extract the effective data.Finally,the spectral-spatial transformer(SST)network is used for pixel classification to determine the pixel position of the underwater target.In this paper,a hyperspectral UAV is used to collect underwater target data,and the UAV hyperspectral data are collected at target depths of 0.1 m,2 m,3 m,and 5 m,respectively.The experimental results show that the proposed algorithm can accurately identify underwater targets.
关键词
水下目标识别/高光谱图像/无人机遥感/transformer
Key words
recognition of underwater target/hyperspectral image/UAV remote sensing/transformer
维普
万方数据