Small and Dim Target Detection in IR Imagery: A Review

While there has been significant progress in object detection using conventional image processing and machine learning algorithms, exploring small and dim target detection in the IR domain is a relatively new area of study. The majority of small and dim target detection methods are derived from conventional object detection algorithms, albeit with some alterations. The task of detecting small and dim targets in IR imagery is complex. This is because these targets often need distinct features, the background is cluttered with unclear details, and the IR signatures of the scene can change over time due to fluctuations in thermodynamics. The primary objective of this review is to highlight the progress made in this field. This is the first review in the field of small and dim target detection in infrared imagery, encompassing various methodologies ranging from conventional image processing to cutting-edge deep learning-based approaches. The authors have also introduced a taxonomy of such approaches. There are two main types of approaches: methodologies using several frames for detection, and single-frame-based detection techniques. Single frame-based detection techniques encompass a diverse range of methods, spanning from traditional image processing-based approaches to more advanced deep learning methodologies. Our findings indicate that deep learning approaches perform better than traditional image processing-based approaches. In addition, a comprehensive compilation of various available datasets has also been provided. Furthermore, this review identifies the gaps and limitations in existing techniques, paving the way for future research and development in this area.Comment: Under Revie

How to find real-world applications for compressive sensing

The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast growing area of research. However, research translating CS theory into practical hardware and demonstrating clear and significant benefits with this hardware over current, conventional imaging techniques has been limited. This article helps researchers to find those niche applications where the CS approach provides substantial gain over conventional approaches by articulating lessons learned in finding one such application; sea skimming missile detection. As a proof of concept, it is demonstrated that a simplified CS missile detection architecture and algorithm provides comparable results to the conventional imaging approach but using a smaller FPA. The primary message is that all of the excitement surrounding CS is necessary and appropriate for encouraging our creativity but we all must also take off our "rose colored glasses" and critically judge our ideas, methods and results relative to conventional imaging approaches.Comment: 10 page

Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept

Registration of Airborne Infrared Images using Platform Attitude Information

In current warfare scenario stealth and passive threat detection capabilities are considered as prime requirements to accomplish desired mission by the fighter aircrafts. To improve the stealth of an aircraft, the trend is towards detecting threats with the help of passive sensors (Electro Optic or Infrared). Current situation caters for systems like Infra-red Search and Track (IRST) and Passive Missile Warning Systems (PMWS). IRST system is a passive target detection system, used for detecting aerial & ground targets. PMWS is a threat detection system used for detecting missiles approaching towards aircraft. Both of these systems detect targets of interest by processing IR images acquired in mid-IR region. The prime challenge in IRST system or PMWS is detecting a moving target of size typically 1~2 pixels in acquired image sequences. The temporal change caused by moving target in consecutive frames can be considered as one important factor to detect them. The temporal change caused by moving target is identified through absolute frame differencing of successive frames. This principle has limitation in application to IRST & PMWS as the imaging sensor with the aircraft is moving. This motion also imparts temporal change in the acquired images. In this paper authors are proposing a method for removing the temporal change caused by the platform motion in two consequently acquired frames using registration process.  The proposed method uses the platform attitude information at frame sampling times. Authors have analyzed the sensitivity of registration process to noisy platform attitude information.Defence Science Journal, 2014, 64(2), pp. 130-135. DOI: http://dx.doi.org/10.14429/dsj.64.546

Image Pre-processing Algorithms for Detection of Small/Point Airborne Targets

The problem of detecting small/point targets in infra-red imagery is an important research area for defence applications. The challenge is to achieve high sensitivity for detection of dim point like small targets with low false alarms and high detection probability. To detect the target in such scenario, pre-processing algorithms are used to predict the complex background and then to subtract predicted background from the original image. The difference image is passed to the detection algorithm to further distinguish between target and background and/or noise. The aim of the study is to fit the background as closely as possible in the original image without diminishing the target signal. A number of pre-processing algorithms (spatial, temporal and spatio-temporal) have been reported in the literature. In this paper a survey of different pre-processing algorithm is presented. An improved hybrid morphological filter, which provides high gain in signal-to-noise plus clutter ratio (SCNR), has been proposed for detection of small/point targets.Defence Science Journal, 2009, 59(2), pp.166-174, DOI:http://dx.doi.org/10.14429/dsj.59.150

Airborne Infrared Search and Track Systems

Infrared search and track (IRST) systems are required for fighter aircraft to enable them to passively search, detect, track, classify, and prioritise multiple airborne targets under all aspects, look-up, look-down, and co-altitude conditions and engage them at as long ranges as possible. While the IRST systems have been proven in performance for ground-based and naval-based platforms, it is still facing some technical problems for airborne applications. These problems arise from uncertainty in target signature, atmospheric effects, background clutter (especially dense and varying clouds), signal and data processing algorithms to detect potential targets at long ranges and some hardware limitations such as large memory requirement to store and process wide field of view data. In this paper, an overview of airborne IRST as a system has been presented with detailed comparative simulation results of different detectionitracking algorithms and the present status of airborne IRST

The Rogue Alpha and Beta Mission: Operations, Infrared Remote Sensing, LEO Data Processing, and Lessons Learned From Three Years on Orbit With Two Laser Communication-Equipped 3U CubeSats

The Aerospace Corporation\u27s Rogue-alpha, beta program was a rapid prototyping demonstration aimed at building and deploying an infrared remote sensing capability into low Earth orbit within 18 months. The two satellites and their data were then used for three years as an experimental testbed for future proliferated low Earth orbit (pLEO) constellations. Their launch took place on November 2, 2019, followed by boost and deployment of two identical spacecraft (Rogue-alpha and beta) by the Cygnus ISS cargo vessel into circular 460-km, 52° inclined orbits on January31, 2020. The primary sensors were 1.4-micron band, InGaAs short wavelength infrared (SWIR) cameras with640x512 pixels and a 28° field-of-view. The IR sensors were accompanied by 10-megapixel visible context cameras with a 37° field-of-view. Star sensors were also tested as nighttime imaging sensors. Three years of spacecraft and sensor operations were achieved, allowing a variety of experiments to be conducted. The first year focused on alignment and checkout of the laser communication systems, sensor calibration, and priority IR remote sensing objectives, including the study of Earth backgrounds, observation of natural gas flares, and detection of rocket launches. The second year of operations added study of environmental remote sensing targets, including severe storms, wildfires, and volcanic eruptions, while continuing to gather Earth backgrounds and rocket launch observations. The final year emphasized advanced data processing and exploitation techniques applied to collected data, using machine learning and artificial intelligence for tasks such as target tracking, frame co-registration, and stereo data exploitation. Mission operations continued in the final year, with an emphasis on collecting additional rocket launch data, and higher frame rate backgrounds data. This report summarizes the Rogue alpha, beta mission’s outcomes and presents processed IR data, including the detection and tracking of rocket launches with dynamic Earth backgrounds, embedded moving targets in background scenes, and the use of pointing-based registration to create fire line videos of severe wildfires and 3D scenes of pyrocumulonimbus clouds. Lessons learned from the experimental ConOps, data exploitation, and database curation are also summarized for application to future pLEO constellation missions

A flexible algorithm for detecting challenging moving objects in real-time within IR video sequences

Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City

Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

Imaging sensors operating in infrared (IR) region of electromagnetic spectrum are gaining importance in airborne automatic target recognition (ATR) applications due to their passive nature of operation. IR imaging sensors exploit the unintended IR radiation emitted by the targets of interest for detection. The ATR systems based on the passive IR imaging sensors employ a set of signal processing algorithms for processing the image information in real-time. The real-time execution of signal processing algorithms provides the sufficient reaction time to the platform carrying ATR system to react upon the target of interest. These set of algorithms include detection, tracking, and classification of low-contrast, small sized-targets. Paper explained a signal processing framework developed to detect and track moving point targets from the acquired IR image sequences in real-time.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.208-213, DOI: http://dx.doi.org/10.14429/dsj.65.816