A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

Abstract

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter.

Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images

“…dimension. The rich spectral information embedded within the HSI is pivotal in identifying subtle differences among different objects, making HSIs widely used across various applications, such as target detection [4], [5], anomaly detection [6], [7], [8], classification [9], and band selection [10], [11], and et al Hyperspectral target detection, in particular, is often approached as a binary classification problem, determining the probability of each pixel as either background or target by the prior target spectrum of interest. However, the intricate imaging environments coupled with low spatial resolution [12] often make it difficult or even impossible to acquire desired spectral information for the target of interest.…”