School of Information Engineering, Chaohu University, Chaohu 238024, China.
School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China.
Comput Intell Neurosci. 2022 May 17;2022:9223552. doi: 10.1155/2022/9223552. eCollection 2022.
Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.
高光谱遥感图像中的异常目标检测是图像研究的热点之一。检测过程中产生的图像噪声会导致高光谱遥感图像质量下降。针对这一问题，本文提出了一种基于卷积神经网络的高光谱遥感图像异常目标检测方法。首先，利用深度残差学习网络模型去除高光谱遥感图像中的噪声。其次，利用高光谱遥感图像的空间和光谱特征来优化聚类字典，然后完成包含目标信息的图像分割。最后，将图像输入具有双分类器的深度卷积神经网络，网络检测图像中的异常目标。该算法的测试结果表明，去噪图像的结构相似性高于 0.86，这表明该方法具有良好的降噪性能，不会损坏图像细节，分割效果良好，能够获得高清晰度的目标图像信息，并准确检测图像中的异常目标。
