.png){kind=icon}
返回
Collaborative representation based unsupervised CNN for hyperspectral anomaly detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.infrared.2024.105498.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The convolutional neural networks (CNNs) have shown high success in supervised analysis of hyperspectral images. But, the use of a supervised CNN is not possible for an unsupervised task such as hyperspectral anomaly detection. So, an unsupervised CNN with pre-determined convolutional kernels without requirement to labeled samples or training process is proposed in this work. The proposed method uses the collaborative representation (CR) for background estimate and introduces the global preserving projection (GPP) for dimensionality reduction of it. Then, the convolutional kernels are randomly selected from the reduced CR data. Moreover, two distances in inner and guard windows are defined, which difference of them results in the anomaly score. The CR based unsupervised CNN (CUCNN) method achieves high detection accuracy compared to its counterparts and is more than 9 times faster than other presented unsupervised CNN detectors.
Keyword:
Unsupervised CNN
Hyperspectral anomaly detection
Collaborative representation
Feature reduction
期刊
I
IF:
3.4
论文数:
5.8K
被引数:
1.2W
机构
引用论文
Hyperspectral anomaly detection based on variational background inference and generative adversarial network.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detectionNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏Segment-driven anomaly detection in hyperspectral data using watershed technique基于分水岭技术的高光谱数据分段驱动异常检测
OAAI
OAAI分享收藏A random patches based edge preserving network for land cover classification using Polarimetric Synthetic Aperture Radar images基于随机补丁的边缘保持网络，用于使用极化合成孔径雷达图像进行土地覆盖分类
PREAI
分享收藏
查看更多内容