Cloud Clutter Research Articles

Detection of a target hidden in dense cloud clutter using an echo pattern of pulsed laser radar systems

The performance of pulsed laser radar systems has been known to be drastically reduced in dense clutter media, detecting false signals instead of the target. Therefore, high-performance laser radars have become demanding for detection applications. Here, using a multi-scattering Monte Carlo simulation, the pulsed laser radar performance is studied in dense cloud clutter (DCC), involving the role of the clutter extinction coefficient in the cloud return signal waveform of the target. After generating a large number of return signals from the clutter and the target, a unique and prominent feature is found to efficiently distinguish the two signals from each other with a resolution of 0.80 and a minimum signal to clutter ratio of 5.04 dB. A high-pass trapezoidal filter is also designed based on the characteristic feature in order to eliminate the clutter return signal. This proposed filtering method is justified by applying Fourier transform to the return power from the target and cloud, eliminating the clutter frequency response in a low frequency region. Accordingly, it is possible to detect a target hidden in DCC at short distances through receiving an echo pulse pattern.

Target Tracking based on Kernelized Correlation Filter Using MWIR and SWIR Sensors

When tracking small UAVs and drone targets in cloud clutter environments, MWIR sensors are often unable to track targets continuously. To overcome this problem, the SWIR sensor is mounted on the same gimbal. Target tracking uses sensor information fusion or selectively applies information from each sensor. In this case, parallax correction using the target distance is often used. However, it is difficult to apply the existing method to small UAVs and drone targets because the laser rangefinder's beam divergence angle is small, making it difficult to measure the distance. We propose a tracking method which needs not parallax correction of sensors. In the method, images from MWIR and SWIR sensors are captured simultaneously and a tracking error for gimbal driving is chosen by effectiveness measure. In order to prove the method, tracking performance was demonstrated for UAVs and drone targets in the real sky background using MWIR and SWIR image sensors.

Dim Moving Point Target Detection in Cloud Clutter Scenes Based on Temporal Profile Learning

This letter presents an approach for detecting dim moving point targets in cloud clutter scenes based on temporal profile learning. The main idea is that a weak transient disturbance will appear in the temporal profiles of target-present pixels, changing the temporal profile’s characteristics. We propose a novel signal-to-signal network to learn the temporal characteristics of the background and the clutter, in which the transient disturbance is extracted by the residual between the input signal and the reconstructed background and clutter signal. The structure of the ConvBlock-1D is designed to enhance the flow and propagation of features in layers. A loss function is proposed to solve the imbalance problem. We provided a comparison to other widely used methods by using simulated datasets and real-world image sequences. The experimental results demonstrate that our method has the best performance in terms of the qualitative and quantitative assessments.

An Efficient and Uncertainty-Aware Decision Support System for Disaster Response Using Aerial Imagery

Efficient and robust search and rescue actions are always required when natural or technical disasters occur. Empowered by remote sensing techniques, building damage assessment can be achieved by fusing aerial images of pre- and post-disaster environments through computational models. Existing methods pay over-attention to assessment accuracy without considering model efficiency and uncertainty quantification in such a life-critical application. Thus, this article proposes an efficient and uncertain-aware decision support system (EUDSS) that evolves the recent computational models into an efficient decision support system, realizing the uncertainty during building damage assessment (BDA). Specifically, a new efficient and uncertain-aware BDA integrates the recent advances in computational models such as Fourier attention and Monte Carlo Dropout for uncertainty quantification efficiently. Meanwhile, a robust operation (RO) procedure is designed to invite experts for manual reviews if the uncertainty is high due to external factors such as cloud clutter and poor illumination. This procedure can prevent rescue teams from missing damaged houses during operations. The effectiveness of the proposed system is demonstrated on a public dataset from both quantitative and qualitative perspectives. The solution won the first place award in International Overhead Imagery Hackathon.

Unsupervised Infrared Small-Object-Detection Approach of Spatial–Temporal Patch Tensor and Object Selection

In this study, an unsupervised infrared object-detection approach based on spatial–temporal patch tensor and object selection is proposed to fully use effective temporal information and maintain a balance between object-detection performance and computation time. Initially, a spatial–temporal patch tensor is proposed by performing median pooling function on patch tensors generated from consecutive frames to suppress sky or cloud clutter. Then, a contrast-boosted approach that incorporates morphological operations is proposed to improve the contrast between objects and background. Finally, an object-selection approach is proposed based on the cluster center derived from clustering locations and gray values, thereby decreasing the search scope of objects in the detection process. The experiments of five infrared sequence frames confirm that the proposed framework can obtain better results than most previous methods when handling heterogeneous scenes in terms of gray values. Experimental results of five real sequence frames also demonstrate that the spatial–temporal patch tensor, the contrast-boosted approach, and object-selection approach can increase the recall ratio by 6.7, 2.21, and 1.14 percentage units and the precision ratio by 1.61, 3.44, and 11.79 percentage units, respectively. Moreover, the proposed framework can achieve an average F1 score of 0.9804 with about 1.85 s of computation time, demonstrating that it can obtain satisfactory object-detection performance with relatively low computation time.

다중센서 정보를 이용한 구름 클러터 환경의 대공표적 추적

불법적으로 운용되는 소형 무인기 혹은 드론을 탐지 및 추적하는 것은 공항 및 발전소와 같은 중요 시설물 보안에서 중요한 임무가 되고 있다. 구름이 있는 하늘 배경에서 드론을 추적하기 위해 단일 센서가 사용 되면 환경변화에 민감하여 표적을 추적하는 것이 매우 어렵다. 그러므로 이런 응용 분야에서는 다중 센서가 선택적으로 사용된다. 제안한 방법은 표적까지의 거리를 측정하기 위해 레이저 거리측정기를 사용하고, 표적을 추적하기 위해 중적외선 및 단파장 적외선 영상센서를 사용한다. 특히, 제안한 방법은 커널 상관 필터 알고리즘을 기반으로 하여, 거리측정 데이터, 중적외선 영상 및 단파장 적외선 영상을 사용하기 때문에 스케일 및 환경변화에 둔감하다. 실험을 통해서 실제 환경에서 획득된 거리정보 및 두 센서의 영상정보를 사용하여 제안한 방법의 유효성을 입증하였다. 또한, 제안한 방법은 구름 클러터의 가변 환경에서 표적을 안정적으로 추적할 수 있었다.

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

To improve the detection of dim and small targets in infrared (IR) images containing high-intensity cloud clutter, a novel adaptive background suppression method is proposed. By using three-dimensional cooperative filtering and differential calculation, the different and complex background clutter is suppressed. To obtain the optimal parameters for the background suppression algorithm, an adaptive parameter optimization method is proposed. The adaptive parameter optimization problem is transformed into a multiobjective optimization problem in which the signal-to-clutter ratio gain and background suppression factor, which effectively reflect the background suppression performance, are chosen as the optimization objectives, and the parameters of the proposed background suppression algorithm are considered as the variables. To effectively solve the established multiobjective optimization problem, a particle swarm parameter optimization-based method is utilized. Experimental results indicate that the proposed adaptive background suppression method using these optimal parameters has good performance for IR images in real complex scenes, as well as performance superior to that of other baseline methods.

Infrared dim and small target detection based on three-dimensional collaborative filtering and spatial inversion modeling

The dim and small target detection in complex background is a challenging problem. Conventional methods may lead to high false alarm rates when targets are extremely dim and almost approximate to background noise. In this paper, a novel method named three-dimensional collaborative filtering and spatial inversion (3DCFSI) is proposed to deal with this problem. In order to suppress complex background clutter, a method based on three-dimensional cooperative filtering and differential calculation is first adopted. Then, a method based on energy accumulation is designed to enhance the dim target signal and improve the SNR. Finally, according to the constant false-alarm ratio judging and statistical method, target detection is realized by a spatial inversion model based on motion parameters. Experiments on real scenes demonstrate that the proposed method is able to stably detect small targets under different cloud clutter and background noise. Meanwhile, the proposed method has achieved more competitive performance than baseline methods.

Point target detection based on multiscale morphological filtering and an energy concentration criterion.

The research on optical imaging characteristics of infrared dim point targets in the presence of nonstationary cloud clutter and random noise is necessary for target detection. We analyze the energy concentration of point targets that are less than 3×3 pixels in size and deduce a simulation model of the point target imaging process. Then we adopt omnidirectional multiscale structural elements to detect all the possible targets distributing in every direction. The adaptive threshold and the energy concentration criterion are employed to eliminate false alarms. Finally, the trajectory of point targets is obtained after the low-order recursive correlation. The results show that the detection probability of the proposed method reaches 99.8% with 0.2% false alarm probability. It demonstrates that the proposed method has a good performance to suppress complex background and random noise. Also, it has the advantage of low complexity and easy implementation in a real-time system.

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the “XL” USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

Parametric temporal compression of infrared imagery sequences containing a slow-moving point target.

Infrared (IR) imagery sequences are commonly used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research focuses on slow-moving point targets that are less than one pixel in size, such as aircraft at long range from a sensor. Since transmitting IR imagery sequences to a base unit or storing them consumes considerable time and resources, a compression method that maintains the point target detection capabilities is highly desirable. In this work, we introduce a new parametric temporal compression that incorporates Gaussian fit and polynomial fit. We then proceed to spatial compression by spatially applying the lowest possible number of bits for representing each parameter over the parameters extracted by temporal compression, which is followed by bit encoding to achieve an end-to-end compression process of the sequence for data storage and transmission. We evaluate the proposed compression method using the variance estimation ratio score (VERS), which is a signal-to-noise ratio (SNR)-based measure for point target detection that scores each pixel and yields an SNR scores image. A high pixel score indicates that a target is suspected to traverse the pixel. From this score image we calculate the movie scores, which are found to be close to those of the original sequences. Furthermore, we present a new algorithm for automatic detection of the target tracks. This algorithm extracts the target location from the SNR scores image, which is acquired during the evaluation process, using Hough transform. This algorithm yields a similar detection probability (PD) and false alarm probability (PFA) of the compressed sequences and the original sequences. The parameters of the new parametric temporal compression successfully differentiate the targets from the background, yielding high PDs (above 83%) with low PFAs (below 0.043%) without the need to calculate pixel scores or to apply automatic detection of the target tracks.

Non-local and nonlinear background suppression method controlled by multi-scale clutter metric

To improve the detection performance for non-morphological multi-scale target in IR image containing complex cloud clutter, on basis of cloud scenario self-similarity feature, a non-local and nonlinear background suppression algorithm controlled by multi-scale clutter metric is presented. According to the classical achievements on cloud structure, self-similarity and relativity of cloud clutter on image for target detection is deeply analyzed by classical indicators firstly. Then we establish multi-scale clutter metric method based on LoG operator to describe scenes feature for controlled suppression method. After that, non-local means based on optimal strength similarity metric as non-local processing, and multi-scale median filter and on minimum gradient direction as local processing are set up. Finally linear fusing principle adopting clutter metric for local and non-local processing is put forward. Experimental results by two kinds of infrared imageries show that compared with classical and similar methods, the proposed method solves the existing problems of targets energy attenuation and suppression degradation in strongly evolving regions in previous methods. By evaluating indicators, the proposed method has a superior background suppression performance by increasing the BSF and ISCR 2 times at least.

Small infrared target detection by region-adaptive clutter rejection for sea-based infrared search and track.

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate.

Background suppression for cloud clutter using temporal difference projection

To remove high intensity cloud clutter in infrared image sequence containing point target with high velocity, based on the optimal log-likelihood ratio detector test (LLRDT) together with exploratory temporal data analysis, a method called standardized maximum projection of temporal difference on adjacent frames (SMPTDAF) is proposed. First, cloud scenario is classified and analysis according to temporal features. Second, mathematical difference models of adjacent frames for all regions are presented. Third, to obtain the optimal temporal performance under LLRDT operator, based on the models, projection method after differencing and its simplified method for practical application are established. Finally in the paper, we compared the proposed method against classical temporal suppression method named Moving Target Indicator (MTI) and wavelet method by test image sequence. Experimental results show that the average SCR gain exceeds 11 when the target SCR is from 1.0 and 3, which is better than results of some representative multi-frame filters mentioned above.

Compression of infrared imagery sequences containing a slow-moving point target, part II

Infrared (IR) imagery sequences are commonly used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research concentrates on slow-moving point targets that are less than one pixel in size, such as aircraft at long ranges from a sensor. Because transmitting IR imagery sequences to a base unit or storing them consumes considerable time and resources, a compression method that maintains the point-target detection capabilities is highly desirable. In our previous work, we introduced two temporal compression methods that preserve the temporal profile properties of the point target in the form of discrete cosine transform (DCT) quantization and parabola fit. In the present work, we extend the compression task method of DCT quantization by applying spatial compression over the temporally compressed coefficients, which is followed by bit encoding. We evaluate the proposed compression method using a signal-to-noise ratio (SNR)-based measure for point target detection and find that it yields better results than the compression standard H.264. Furthermore, we introduce an automatic detection algorithm that extracts the target location from the SNR scores image, which is acquired during the evaluation process and has a probability of detection and a probability of false alarm close to those of the original sequences. We previously determined that it is necessary to establish a minimal noise level in the SNR-based measure to compensate for smoothing that is induced by the compression. Here, the noise level calculation process is modified in order to allow detection of targets traversing all background types.

Compression of infrared imagery sequences containing a slow-moving point target

Infrared imagery sequences are used for detecting moving targets in the presence of evolving cloud clutter or background noise. This research concentrates on slow-moving point targets that are less than one pixel in size, such as aircraft at long ranges from a sensor. The infrared (IR) imagery sequences that are captured by ground sensors contain an enormous amount of data. Since transmitting this data to a base unit or storing it consumes considerable time and resources, a compression method that maintains the point target detection capabilities is desired. For this purpose, we developed two temporal compression methods that preserve the temporal profile properties of the point target. We evaluated the proposed compression methods using a signal-to-noise-ratio (SNR)-based measure for point target detection and showed that the compression may improve the SNR results compared to the IR sequence prior to compression.

DETECTION OF MOVING SMALL TARGETS IN INFRARED IMAGE SEQUENCES CONTAINING CLOUD CLUTTER

Detecting and tracking dim moving small targets in infrared image sequences containing cloud clutter is an important area of research. The paper proposes a novel algorithm for the dim moving small target detection in cloudy background. The algorithm consists of three courses. The first course consists of the image spatial filtering and the sequence temporal filtering, it can be realized by two parallel calculative parts. The second course is the fusion and the segmentation processing. The last course is the targets acquiring and tracking, it can be achieved by the Kalman tracker. The results of our experiment prove that the algorithm is very effective.

Development of the local maximum variety of ranked-order filters.

A form of ranked-order filters is introduced as the local maximum filter. The construction of the local maximum filter is described, followed by a discussion of its function and some of its more important properties, and an example application of a two-dimensional local maximum filter is provided to illustrate the detection of single-pixel targets against a cloud clutter background. The closing discussion provides a mathematical development of the filter.

Point target detection in IR image sequences: a hypothesis-testing approach based on target and clutter temporal profile modeling

We approach the problem of point target detection in infrared image sequences by modeling the temporal behavior of clutter and targets on a single-pixel basis. These models, which are experimentally verified, are then used to develop a temporal likelihood-ratio test and derive the corresponding decision rule. We demonstrate the effectiveness of the technique by applying it to real infrared image sequences containing targets of opportunity and evolving cloud clutter. The physical models and resulting hypothesis-testing approach could also be applicable to other image-sequence-processing scenarios, using acquisition systems besides infrared imaging, such as the detection of small moving objects or structures in a biomedical or biological imaging scenario, or the detection of satellites, meteors, or other celestial bodies in night-sky imagery acquired using a telescope.

Temporal filters for tracking weak slow point targets in evolving cloud clutter

A class of temporal filters is presented for use with a staring infrared camera in detecting and tracking weak point targets moving slowly in evolving cloud clutter. The generic temporal filter, originally suggested by the singular value decomposition of consecutive frame data, is a zero mean damped sinusoid which can be recursively implemented in the complex plane. From this filter type, a composite triple temporal filter (TTF) is developed, consisting of two sinusoids of different periods in sequence followed by a third (averaging) filter. The TTF achieves impressive cloud clutter suppression by responding strongly to pixel temporal responses caused by moving point targets and weakly to responses caused by cloud edges moving into or out of pixels. An extensive database of local airfield scenes with targets of opportunity taken with two laboratory staring IR cameras was used in the design and testing of the filters. Issues and trade-offs in choosing the parameters of the TTF are explored by comparing two specific forms of the filter: the first based on a damped sinusoid with a period of 16 frames followed by one with a 10 frame period; the second filter has corresponding periods of 40 followed by 30 frames. The first TTF is very effective with targets having velocities from 0.1–0.5 pixels/frame in daytime drifting cloud scenes. However, target signal-to-noise values of ⩾6 are required for detection in white noise (close to blue-sky conditions). The second TTF is more sensitive to slower, weaker targets in blue-sky or cloudless night scenes; however, in order to operate in daytime cloud scenes, spatial enhancements are required. Results are detailed for some representative scenes and given as well for the total database as signal-to-clutter gain plots based on a newly formulated antimedian metric.