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

Liu, Chang; Xie, Fengying; Dong, Xiaomeng; Gao, Hongxia; Zhang, Haopeng

doi:10.1109/jstars.2022.3151928

Cited by 19 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The global extremum of the particle population leads the whole population to move towards the adjacent area of the optimal solution to the problem. During continuous iterations, the particle population adjusts the flight speed and direction through Equation ( 8) and updates its position according to Equation (9). Finally, obtaining the optimal solution that satisfies the termination condition.…”

Section: Otsu Optimization Based On Apso Algorithmmentioning

confidence: 99%

“…Therefore, the accurate and fast extraction of small targets from coal and gangue dual-energy X-ray images is a crucial subject in this field. To detect small targets with low contrast, Liu et al [9] proposed a local adaptive threshold detection algorithm, which computes segmentation thresholds in small local windows and effectively reduces the undetected rate of the target. Qin et al [10] addressed the target detection problem in terms of local image segmentation, filtered by facet to enhance the target pixel, and then extracted candidate target pixels through adaptive threshold to achieve small target detection.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

Zhao

Wang

Guo

et al. 2022

Preprint

View full text Add to dashboard Cite

The positioning accuracy of coal and gangue is related to the discharge accuracy of gangue, which will affect the utilization rate of coal. But the detectability of the small coal and gangue is poor due to the fewer number of pixels and texture information in coal and gangue dual-energy X-ray images. So, the Otsu with crotch structure based on Adaptive Partical Swarm Optimization (APSO) for small targets detection is proposed, called after APSO-C_Otsu. Firstly, the Otsu with crotch structure is used to perform multi-threshold segmentation of coal and gangue dual-energy X-ray images to increase the contrast between small target and background. Meanwhile, the APSO algorithm was used to optimize the Otsu algorithm with crotch structure in order to improve its convergence speed and reduce its calculation amount. Finally, the processed image is binarized, and the location of the target was labeled based on the bwlabel algorithm. The experimental results revealed that the APSO-C_Otsu algorithm could effectively detect the small pixel size (less than 8 × 8 pixels) of coal and gangue with a particle size of 6 ~ 30 mm, and was also applicable to the coal and gangue with the particle size larger than 30 mm, which was of great significance for accurate separation of coal and gangue and the improvement of coal utilization.

show abstract

Section: Otsu Optimization Based On Apso Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

Zhao

Wang

Guo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Liu et. al [14] enhanced image by compactness filter and used a local threshold to segment candidate region. On the basis of SExtractor, Teeninga et.…”

Section: Introductionmentioning

confidence: 99%

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

Wang,

Chen,

Shen

et al. 2024

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

View full text Add to dashboard Cite

With the increasing number of space debris in nearearth space, the surveillance of space has become more crucial to prevent potential space collisions. In an optical surveillance system, a multitude of debris with varying sizes, speeds and intensities exist within images, which brings great difficulties for the extraction of targets. Current methodologies mainly emphasize local saliency enhancement and source extraction based on global statistical attributes, yet often overlook the distribution similarity of individuals and how to map them in a standardized space. To address this limitation, our paper introduces an innovative transformation from grayscale space to correlation space. Initially, a fourfold-structure model is designed to measure the local correlation for each element in the grayscale space. Subsequently, the standardized correlation space is constructed by correlation measurement. Then, combining an adaptive threshold choosing method based on statistics and a threshold limitation strategy based on correlation, the individuals can be separated from the feature space. Finally, a deblending method is applied to disentangle merged individuals based on the local gradient correlation. Thoroughly assessed using ten subimage sequences featuring targets with typical traits against complex backgrounds, the results confirm its superior extraction capabilities and effectiveness when compared to ten common baseline methods, which achieves an average 90.9% true positive rate in just 2.8 ms. Furthermore, the extraction outcomes serve as a basis for detecting true target trajectories in continuous sequences. This integration holds significant implications for practical applications in space surveillance engineering.

show abstract

“…The infrared sensing image is often affected by background clutter and detection distance, resulting in a low signal-to-clutter ratio (SCR) scene. As a result, dim small targets may be submerged in background clutter and noise, making target detection more challenging [4]. Recent years have seen significant improvements in the performance of infrared imaging equipment, enabling the capture of high-quality single-frame infrared images that provide the basic support for detecting infrared small targets [5].…”

Section: Introductionmentioning

confidence: 99%

Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

Lizhe

Zhang

et al. 2023

Electronics

View full text Add to dashboard Cite

Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness.

show abstract

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

Cited by 19 publications

References 41 publications

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

Contact Info

Product

Resources

About