SAR Image Change Detection Using Saliency Extraction and Shearlet Transform

Zhang, Yan; Wang, Shigang; Wang, Chao; Li, Jin-xin; Zhang, Hong

doi:10.1109/jstars.2018.2866540

Cited by 22 publications

(21 citation statements)

References 31 publications

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“…Inspired by [11], we use simple equal-weight fusion to compress the log-ratio and mean ratio difference images, thereby improving the quality of the difference image and the change detection accuracy while reducing the run time. A simple equal weight fusion difference image R can be obtained by the following formula (19):…”

Section: B Generation Of Difference Imagementioning

confidence: 99%

“…The pixels in the difference image were pre-classified and used to train the network. Saliency extraction has also recently been applied in change detection [18,19]. Most current change detection algorithms successfully suppress the influence of speckle noise on change detection of a remote sensing image but do not significantly affect sensor noise in a low-illumination monitoring image.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Generation Of Difference Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Change Detection in Multitemporal Monitoring Images Under Low Illumination

et al. 2020

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“…Furthermore, the ratio mean detector [11] based on the ratio of local intensity means of pixel patches can enhance the low-intensity pixels, which is also robust to speckle noise. There are some other works proposed recently to generate a better DI-based on the fusion of different methods, such as the wavelet fusion technique on both log-ratio and mean-ratio images [12]- [14], the wavelet fusion technique on Gauss-log ratio and log-ratio images [15], the saliency extraction guided log-ratio images [16], the shearlet fusion technique on saliency extraction, and Gauss-log ratio images [17]. Due to the presence of the speckle noise, it is difficult to keep tradeoff between robustness to noise and effectiveness of preserving the detail [18].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Change Detection Based on Nonlocal Low-Rank Model and Two-Level Clustering

Sun

Lei

Guan

et al. 2020

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

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Change detection (CD) has found a wide range of applications in many fields. In this article, we propose a novel nonlocal low-rank (NLR) based method for multitemporal synthetic aperture radar image CD. This method jointly exploits the powerful NLR-based despeckling and the effective cascade clustering. First, the NLR model is used to generate the difference image (DI), which consists of a patch grouping process and a low-rank minimizing process. Especially, the NLR minimization model contains a data fidelity term, which is based on the statistical distribution of speckle noise, and a regularization term, which uses the weighted nuclear norm. Then, the alternating direction methods of multipliers is introduced to solve this minimization problem. Second, after DI is generated, the principal component analysis is employed to extract the feature and a two-level clustering method is used to generate the final change map, which separates the intermediate class by using the neighbor information with Gaussian weighted distance. Experiment results demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

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“…Amitrano et al [24] provided a unsupervised framework for Sentinel-1 flood mapping combining a textural analysis with a fuzzy classification on GRD amplitudes and a change detection approach, designed for end users and decision makers. Zhang et al [25] estimated the impact of hurricane Irma on Florida in terms of floods using both spectral analysis and InSAR from Sentinel-1 data to quantify the extent of the floods and water level variations. In Bayik et al [26] flooded areas were extracted with threshold, random forest and deep learning approaches on Sentinel-1 time-series at the border of Turkey and Greece.…”

Section: Introductionmentioning

confidence: 99%

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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SAR Image Change Detection Using Saliency Extraction and Shearlet Transform

Cited by 22 publications

References 31 publications

Change Detection in Multitemporal Monitoring Images Under Low Illumination

Change Detection in Multitemporal Monitoring Images Under Low Illumination

SAR Image Change Detection Based on Nonlocal Low-Rank Model and Two-Level Clustering

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

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