Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Abstract

Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this paper, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the 1 L norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimizationbased methods, this method has better detection performance and accuracy.

Infrared Cirrus Detection Using Non-Convex Rank Surrogates for Spatial-Temporal Tensor

“…The comparison indicators are the ROC curves, PR curves and F-measure values of various methods. The following methods are compared: IPI [44], LOGTFNN [30], PSTNN [6], TMESNN [29], KSVD fractal [28] and DivisorstepTP [53]. Figures 12 and 13 shows the ROC curve and PR curve of all methods.…”

“…Machine learning has improved the detection accuracy within a certain range; however, since the real cirrus scene lacks sufficient data to train the model, the method based on machine learning cannot achieve great detection performance in real scenes. Moreover, some scholars also introduced robust principal component analysis (RPCA) to cirrus cloud detection, but due to the design of the models, these studies have not yet achieved satisfactory results, especially in some complex scenarios [28,29].…”

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

“…The detection of small infrared targets, defined by their limited size-often under 9×9 pixels or constituting less than 0.15% of the field of view-poses a significant challenge in IRST applications [9]. These targets are characterized by their minimal texture and shape information, making them difficult to distinguish from complex backgrounds that include terrain, artificial structures, and clouds [10][11][12][13][14]. These backgrounds often reflect solar radiation that can create false alarms with patterns akin to those of the intended targets, as shown in Figure 1, in which, as the scenes become more complex, an increasing number of false alarm sources appear, resulting in the targets being less salient.…”