Modified log-ratio operator for change detection of synthetic aperture radar targets in forest concealment

Gao, Gui; Wang, Xiaoyang; Niu, Min; Zhou, Shilin

doi:10.1117/1.jrs.8.083583

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…Due to the subtraction process, the unchanged parts (static clutter) in OSLI are gathered around zero in histogram of log[F(i)], while the changed parts(moving target signal) are persevered and fall into the tail of the histogram of log[F(i)]. Compare the conventional log-ratio operator in reference [24], expression (13) has the same form. Log-ratio operator is widely used in change detection applications, and the Gaussian distribution is suggested for modeling the ratio image as in [24].…”

Section: Background Image Generating and Subtractionmentioning

confidence: 99%

“…Compare the conventional log-ratio operator in reference [24], expression (13) has the same form. Log-ratio operator is widely used in change detection applications, and the Gaussian distribution is suggested for modeling the ratio image as in [24]. In addition, paper [11] used the real data to prove the effectiveness of modeling the foreground image with this distribution.…”

Section: Background Image Generating and Subtractionmentioning

confidence: 99%

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Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

et al. 2019

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Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

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Section: Background Image Generating and Subtractionmentioning

confidence: 99%

Section: Background Image Generating and Subtractionmentioning

confidence: 99%

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

et al. 2019

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“…Although there is no research report based on the change detection of two multitemporal monitoring images, numerous studies have been carried out on change detection of remote sensing images under noise interference. In recent years, several methods have been developed to reduce the noise in change detection of remote sensing images, such as in [3] and [4]. To better identify the changes between two multitemporal remote sensing images, various methods have been developed to suppress the influence of noise between two multitemporal remote sensing images: the mean ratio difference image [5], the neighborhood ratio difference image [6], the log-ratio difference image [7], and the fusion difference image [8].…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Multitemporal Monitoring Images Under Low Illumination

et al. 2020

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Video surveillance may involve the simultaneous monitoring of a large number of areas. Realtime automatic change detection of a monitoring area (such as involving the movement of people or vehicles) can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) of dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of this paper is to reduce the effect of sensor noise on change detection of monitored images and the run time of change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weighted multiscale series of a morphological filter. Next, two difference images were obtained by using the compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained by equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the fusion difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection results. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrate that the proposed method is superior to reference algorithms in terms of change detection accuracy, time and robustness. INDEX TERMS change detection; morphological structure filtering; normalized fusion difference map; low illumination monitoring image

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“…ese methods are widely used in multitemporal remote-sensing image change detection. For example, Huang et al got the difference image of two temporal remote-sensing images by UID, IR, and mixed method, and then the 2D-OTSU method improved by the firefly algorithm was used to segment the difference image to obtain the change areas [13]; in order to solve the impact of speckle noise on the change detection accuracy of multitemporal SAR images, Gao et al proposed a modified-logratio (MLR) operator for change detection of targets in forest concealment [14]. Nordberg and Evertson used VID combined with the image regression method to study the impact on decrease of vegetation coverage in the Swedish mountains caused by increased mining [15].…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

Huang

Peng

Xin

2020

Journal of Spectroscopy

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In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

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Modified log-ratio operator for change detection of synthetic aperture radar targets in forest concealment

Cited by 13 publications

References 12 publications

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Change Detection in Multitemporal Monitoring Images Under Low Illumination

Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

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