Joint spatial constrained energy minimization for gas identification in hyperspectral imaging

Abstract

Hyperspectral gas identification plays a crucial role in a variety of applications ranging from environmental monitoring to national security. Constrained Energy Minimization (CEM) constitutes a pivotal approach in hyperspectral gas target identification, emphasizing the enhancement of target signal output energy and the suppression of background signal output energy through the design of specialized filters. However, conventional CEM algorithms present notable deficiencies, notably their failure to fully exploit spatial information in hyperspectral image target recognition and their ineffectiveness in detecting targets over extensive areas. To address these challenges, a hyperspectral gas identification method named Joint Spatial Constrained Energy Minimization (JSCEM) was introduced. This method incorporates adaptive weight constraints and employs “pseudo background spectrum” techniques, integrating spatial data to improve target discernment and refining the estimation accuracy of the background autocorrelation matrix. Experimental validations involving both simulated and manual data have shown that the JSCEM method offers marked improvements in the detection of large-area gas targets within hyperspectral image. Simulation data indicate that the detection performance of the JSCEM method is significantly enhanced compared to various existing CEM algorithms. Furthermore, measured data confirm that the JSCEM algorithm accurately identifies large gas targets in hyperspectral images, offering a precise approach to gas detection. This advancement is expected to be valuable in industries requiring accurate and efficient gas identification.