Multi-sensor image fusion based on contrast and directional features optimization

Abstract

Multi-sensor image fusion is always an important and opening problem, which can enhance visual quality and benefit some social security applications. In this article, we use contrast pyramid to decompose visible and infrared images, respectively, and the directional filter banks are applied to obtain multiple directional sub-band image features. Then, we compute the decomposition coefficients of visible and infrared images using a low-pass filter on the decomposed data; and finally, we introduce the whale optimization algorithm to search optimal coefficients to reconstruct the final fusion image. The experiments are conducted on multiple datasets with subjective and objective comparisons, in which the qualitative and quantitative analyses indicate the validity of the proposed method.

A New Approach to Studying Traditional Roof Behaviour in a Changing Climate—A Case Study from the Mediterranean Island of Malta

“…Data Integration and Fusion: Studies over the last ten years have presented interesting results on data integration and fusion by employing quantitative means for multi-sensor remote sensing data sources [69][70][71], which is a stimulus for the current authors to emulate this way forward. The synthesis and interpretation of the overall results from the different remote sensing sensors and platforms, integrated with the direct in-situ air, surface and subsurface data coupled with information on the real composition of the said roofs, will help identify the behaviour of traditional roofs under different environmental conditions, and how these compare to modified, hybrid and modern roofs.…”

An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

“…In recent years, considerable attention has been paid to multisensor data fusion technology or information acquisition and environment sensing, such as the wireless network [1], fault detection [2], condition monitoring [3], and image processing [4][5][6]. Due to the complexity of the targets, the data collected from a single sensor perform poorly when making decisions.…”