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

Chunping Yang,Zhenming Peng,Xuan Kong,Zhaoyang Cao

doi:10.1109/access.2020.3015975

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 article, 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1} $ </tex-math></inline-formula> 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 optimization-based methods, this method has better detection performance and accuracy.

Highlights

Space infrared imaging satellites play an important role in ground monitoring, natural resource exploration and early warning systems, and are a necessary tool for ground surveillance, observation, and early warning and interception of missiles or aircraft [1]
Aiming at the insufficient performance of cirrus detection methods based on physical attributes and its loss of multispectral data internal structural information, and the methods based on artificial intelligence relying on massive data, etc., an infrared image cirrus detection method based on tensor multimode expansion sum nuclear norm is proposed
This method makes full use of small sample data, which uses visual features and sparse and low-rank decomposition to detect cirrus. This method regards multiple bands of remote sensing data as tensors, and combines the tensor multimode expansion sum nuclear norm and l1 norm to construct a tensor decomposition model, transforming the traditional cirrus detection task into a tensor robust principal component analysis model (TRPCA) problem, and tensor multi-mode expansion sum nuclear norm (TMESNN) can represent the tensor rank better compared with existing tensor nuclear norms

Summary

Introduction

Space infrared imaging satellites play an important role in ground monitoring, natural resource exploration and early warning systems, and are a necessary tool for ground surveillance, observation, and early warning and interception of missiles or aircraft [1]. In satellite infrared images, some high-radiation terrain or phenomena in the imaging band has the same gray level as the target, which may cause false alarms in remote sensing early warning system. These terrains or phenomena are called false alarm sources [2]. It is usually weak, and its visual characteristic is similar to infrared military targets, which cause false alarms in remote sensing early warning systems, so the study of cirrus detection in satellite infrared images is necessary

Methods

Results

Conclusion