Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Abstract

The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

Application of Image Processing Methods for IR Intensity Measurement in Spatial and Temporal Domains: A Comprehensive Review

“…Infrared intensity measurement typically focuses on detecting radiation in these specific spectral bands. [10] Thermal Emission: [5] Objects with temperatures above absolute zero emit infrared radiation. The intensity of this thermal emission is directly related to the temperature of the object, following Planck's law of black-body radiation.…”

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

“…Due to the long detection distance and low signal-to-noise ratio of targets, it is difficult to detect targets on the image plane. Therefore, researchers have conducted many theoretical analyses and simulations on the detection and recognition of dim moving targets in deep space backgrounds [7][8][9][10][11]. Satellites located in specific orbits can use the passive observation of IR sensors to obtain Line-of-Sight (LOS) measurement information from the sensor to the target.…”

Sensing and Processing for Infrared Vision: Methods and Applications

“…Moreover, the parameters are appropriately chosen through experiments. Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection [ 7 ]: In this research paper, a new concise method for detecting aerial targets is proposed, which is based on local spatial–temporal matching (LSM). LSM involves four main steps: local normalization, local direction matching, spatial–temporal joint modelling, and inverse matching.…”