Edge and Corner Awareness-Based Spatial–Temporal Tensor Model for Infrared Small-Target Detection

Zhang, Ping; Zhang, Lingyi; Wang, Xiaoyang; Fu, Shencheng; Tian, Pu; Fei, Chun

doi:10.1109/tgrs.2020.3037938

Cited by 68 publications

(28 citation statements)

References 42 publications

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“…where σ in and σ out denote the standard deviations of the full backgrounds in intensities in the input original images and the propocessed images, respectively. Since the BSF index is used to evaluate the effectiveness of background suppression, the target regions are excluded when calculating [14,39]. The higher value of the BSF index, the better background suppression of the algorithm.…”

Section: Resultsmentioning

confidence: 99%

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Small target detection from infrared remote sensing image is a challenge task. In this paper, a novel local adaptive threshold algorithm combined with heterogeneity and compactness filters is proposed to detect the small target from the infrared remote sensing images. Firstly, the infrared image is filtered by a heterogeneity filter to enhance the target saliency. Then, the enhanced image is filtered by a compactness filter to generate target candidate region map. Finally, for each pixel in the target candidate region, a local adaptive threshold is calculated from the enhanced image to determine whether it is a target pixel or not, and thus the targets are extracted out. The designed heterogeneity filter and compactness filter can effectively suppress the background clutter, enhance the target, and generate target candidate regions. The proposed adaptive thresholding is a local threshold method, which is calculated in a small local window and can effectively reduce the false alarm and missing alarm. Qualitative and quantitative experiments are conducted on synthetic images and real images. The experiment results show that, with good target enhancement and background suppression, and high detection accuracy, the proposed method outperforms other state-of-the-art methods.Index Terms-infrared dim small target (IDST), remote sensing image, adaptive threshold, background suppression, target enhancement.

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Section: Resultsmentioning

confidence: 99%

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Multiple subspace learning is adopted to modify [39] in [40]. Taking edge and corner into consideration, Zhang et al [41] proposed a novel spatial-temporal tensor model to detect infrared small target. Motivated by human visual perception, Li et al [42] proposed a novel spatio-temporal saliency approach for dim moving target detection.…”

Section: ) Image Patch Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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“…Some methods extend the optimization problem on 2-D image patches in the spatial domain to 3-D spatial-temporal tensors (STT) [ 32 , 33 , 34 ]. For example, a novel edge and corner awareness-based spatial-temporal tensor (ECS-STT) model [ 35 ] was presented to suppress the strong edge and corner. These algorithms are more effective, but adopting the cues in the temporal dimension increases the convergence time of them.…”

Section: Related Workmentioning

confidence: 99%

Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure

Rao

Chen

et al. 2022

Sensors

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Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.

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Edge and Corner Awareness-Based Spatial–Temporal Tensor Model for Infrared Small-Target Detection

Cited by 68 publications

References 42 publications

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure

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