How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

Wang, Rongfang; Chen, Jiawei; Jiao, Licheng; Wang, Mi

doi:10.3390/rs11040421

Cited by 14 publications

(10 citation statements)

References 70 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned above, misregistration is inevitable for multi-temporal images. Indeed, appropriate feature extraction methods possess adaptability to various multi-source input images, but also minimize the dependence on out-of-detection operations, such as image registration [28]. At present, "from artificial design features to deep learning" and "from the unsupervised to the supervised" are the prominent development trends that present in change information extraction, aiming at recurring to their robustness and self-adaption in feature extraction.…”

Section: Change Detection Frameworkmentioning

confidence: 99%

“…• Noise suppression: Noise interference is an unavoidable problem in image detection, especially for SAR images. Singular value decomposition (SVD) [77] and sparse model [28] can map high-dimensional data space to low-dimensional data space, meanwhile undertaking auxiliary denoising. For example, the adapted sparse constrained clustering (ASCC) method [78] integrates the sparse features into the clustering process, utilizing the coding representation of only meaningful pixels.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

View full text Add to dashboard Cite

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

show abstract

Section: Change Detection Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

View full text Add to dashboard Cite

show abstract

“…In addition, is the slack variable shared by all the constraints which measures training loss. It has been proven [22] that the models in (7) and (8) have the same solutions when = (1/N) N k=1 k is satisfied. Finally, the model in (8) can be efficiently solved by the cutting-plane algorithm [23].…”

Section: B Spatial Metric Learning For Change Detectionmentioning

confidence: 99%

SAR Image Change Detection via Spatial Metric Learning With an Improved Mahalanobis Distance

Wang

Chen

Wang

et al. 2020

IEEE Geosci. Remote Sensing Lett.

Self Cite

View full text Add to dashboard Cite

The log-ratio (LR) operator has been widely employed to generate the difference image for synthetic aperture radar (SAR) image change detection. However, the difference image generated by this pixelwise operator can be subject to SAR images speckle and unavoidable registration errors between bitemporal SAR images. In this letter, we proposed a spatial metric learning method to obtain a difference image that is more robust to the speckle by learning a metric from a set of constraint pairs. In the proposed method, the spatial context is considered in constructing constraint pairs, each of which consists of patches in the same location of bitemporal SAR images. Then, a semidefinite positive metric matrix M can be obtained by the optimization with the max-margin criterion. Finally, we verify our proposed method on four challenging data sets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-the-art methods.Index Terms-Change detection, metric learning, spatial priors, synthetic aperture radar (SAR) image.

show abstract

“…S YNTHETIC aperture radar (SAR) images are usually corrupted with speckle noise, which leads to the degradation of image quality and affects the performance in various applications of remote sensing, e.g., classification [1] and change detection [2]. Several methods have been proposed to mitigate the speckle in SAR images, including filtering methods [3], [4], wavelet shrinkage [5], [6], SAR blockmatching 3-D algorithm (SAR-BM3D) [7].…”

Section: Introductionmentioning

confidence: 99%

A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images

Yuan

Guan

Feng

et al. 2022

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

In this letter, we aim to address synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, a practical solution for SAR despeckling (PSD) is proposed. Firstly, to generate speckled-tospeckled (S2S) image pairs from the same scene in the situation of only single speckled SAR images are available, an adversarial learning framework is designed. Then, the S2S SAR image pairs are employed to train a modified despeckling Nested-UNet model using the Noise2Noise (N2N) strategy. Moreover, an iterative version of the PSD method (PSDi) is also proposed. The performance of the proposed methods is demonstrated by both synthetic speckled and real SAR data. SAR block-matching 3-D algorithm (SAR-BM3D) and SAR dilated residual network (SAR-DRN) are used in the visual and quantitative comparison. Experimental results show that the proposed methods can reach a good tradeoff between speckle suppression and edge preservation.

show abstract

How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

Cited by 14 publications

References 70 publications

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

SAR Image Change Detection via Spatial Metric Learning With an Improved Mahalanobis Distance

A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images

Contact Info

Product

Resources

About