Multidirectional Graph Learning-Based Infrared Cirrus Detection With Local Texture Features

Infrared cirrus detection is extensively used in military and civil fields, but it poses several challenges to existing methods. These challenges include the complex and diverse shapes of the cirrus clouds, as well as their varying sizes. Additionally, false alarms can be easily triggered by the shadows of cirrus clouds and strong edges. Furthermore, dim and weak cirrus clouds blend into the background and lack distinct features, leading to missed detections. This paper presents an innovative infrared cirrus detection model based on multi-directional graph learning and local fractal feature prior weight mapping to overcome these challenges. Taking into account the structural characteristics of cirrus and background continuity, the proposed method utilizes graph learning enhanced matrix decomposition to separate the cirrus clouds and background. Additionally, to highlight cirrus clouds while suppressing strong edges in the background caused by mountains or rivers, weighted local fractal features are proposed as prior knowledge. To improve the detection of dim and small cirrus clouds as well as accelerate the convergence, a reweighting optimization scheme is proposed. The model is solved using the Alternating Direction Method of Multipliers (ADMM) framework. Extensive experiments demonstrate that the proposed scheme outperforms a variety of classic techniques in terms of detection performance.