高光谱成像
非负矩阵分解
端元
人工智能
模式识别(心理学)
图像融合
计算机科学
全色胶片
对数
传感器融合
特征提取
图像(数学)
融合
矩阵分解
数学
计算机视觉
特征向量
物理
量子力学
数学分析
哲学
语言学
作者
Gangshan Wu,Wei Pan,Sicheng Jian,Lin Wang,Zhenyu An
标识
DOI:10.1117/1.jrs.15.036501
摘要
The fusion of a hyperspectral image (HSI) and a panchromatic image (PI) creates data that is beneficial to subsequent processing. Based on the traditional nonnegative matrix factorization (NMF)-based fusion method, the logarithmic hyperbolic cosine function is introduced as a spectral constraint. Under the constraint, the original HSI is decomposed into endmember and abundance matrices. The abundance matrix is then enhanced using PI, and the fused HSI is finally obtained with NMF reconstruction. In this way, the fused data have comparable spatial detail and spectral information. According to the experiments on the simulated and real data, the proposed fusion method obtains balanced fused results, and it is more suitable for the HSI fusion task.
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