ABSTRACT

Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the performance of detectors, especially when the target size is small. Therefore, improving the detection performance of small targets and noisy targets is a key task. Considering the great feature extraction and representation ability of deep learning models, denoising autoencoder (DAE) is introduced to reconstruct spectrums and exploit the invariant information for target detection. To fully extract the features from the original spectrums, a multiscale denoising autoencoder (MSDAE) model is designed to incorporate complementary informationin in this paper. The final spectrum is fused by reconstructed spectrums from different scales representations, which provides more complex information and more robust features for subsequent spectral identification. Results on simulated hyperspectral images (HSIs) and real-world HSIs demonstrate that the proposed MSDAE model can effectively remove noise interference and lead to great improvements of the target detection. In addition, the proposed method shows significant potential in preserving small targets.

摘要

目标检测是高光谱技术的最重要应用之一。但是，由于噪声或环境引起的光谱变化，类内变化会扩大，这会降低检测器的性能，尤其是在目标尺寸较小时。因此，提高小目标和噪声目标的检测性能是一项关键任务。考虑到深度学习模型的强大特征提取和表示能力，引入了降噪自动编码器（DAE）来重构频谱并利用不变信息进行目标检测。为了从原始频谱中充分提取特征，本文设计了一种多尺度降噪自动编码器（MSDAE）模型，以将补充信息纳入其中。最终光谱与来自不同比例表示的重构光谱融合在一起，这为后续光谱识别提供了更复杂的信息和更强大的功能。模拟的高光谱图像（HSI）和真实世界的HSI的结果表明，所提出的MSDAE模型可以有效地消除噪声干扰，并大大提高了目标检测的效率。此外，该方法在保留小目标方面显示出巨大潜力。