scholarly journals PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 304 ◽  
Author(s):  
Tao Xie ◽  
Jingjian Huang ◽  
Qingzhan Shi ◽  
Qingping Wang ◽  
Naichang Yuan
Keyword(s):  
Distance Measure ◽  
Speckle Noise ◽  
Saliency Map ◽  
Spatial Distance ◽  
Gaussian Kernel ◽  
Local Contrast ◽  
Sar Images ◽  
Generating Method ◽  
Contrast Measure ◽  
Almost All

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

scholarly journals SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES BASED ON HEXAGON INITIALIZATION AND EDGE REFINEMENT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1225-1232
Author(s):  
M. Li ◽  
H. Zou ◽  
Q. Ma ◽  
J. Sun ◽  
X. Cao ◽  
...  
Keyword(s):  
Real World ◽  
Distance Measure ◽  
State Of The Art ◽  
Speckle Noise ◽  
Spatial Distance ◽  
Superpixel Segmentation ◽  
Computation Efficiency ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
The Impact

Abstract. Superpixel segmentation for PolSAR images can heavily decrease the number of primitives for subsequent interpretation while reducing the impact of speckle noise. However, traditional superpixel segmentation methods for PolSAR images only focus on the boundary adherence, the significance of superpixel segmentation will be lost when the accuracy is improved at the expense of computation efficiency. To solve this problem, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization and edge refinement. First, the PolSAR image is initialized as hexagonal distribution, where the complexity of searching pixels for relabelling in the local regions can be reduced by 30% theoretically. Second, all pixels in the PolSAR image are initialized as unstable pixels based on the hexagonal superpixels, which can boost the segmentation performance in the heterogeneous regions and effectively maintain all the potential edge pixels. Third, the revised Wishart distance and the spatial distance are integrated as a distance measure to relabel all unstable pixels. Finally, the postprocessing procedure based on a dissimilarity measure is applied to generate the final superpixels. Extensive experiments conducted on both the simulated and real-world PolSAR images demonstrate the superiority and effectiveness of our proposed algorithm in terms of computation efficiency and segmentation accuracy, compared to three other state-of-the-art methods.

scholarly journals Revisiting the effect of spatial resolution on information content based on classification results

2021 ◽  
Author(s):  
MG Palacio ◽  
SB Ferrero ◽  
Alejandro Frery
Keyword(s):  
Information Content ◽  
Classification Accuracy ◽  
Speckle Noise ◽  
Support Vector ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Local Mean ◽  
Almost All ◽  
Processing Techniques ◽  
Source Of Information

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kinds of images, and how it is affected by usual processing techniques. Previous works have resulted in various approaches for quantifying image information content. In this paper, we study this problem from the classification accuracy viewpoint, focusing on the filtering and the classification stages. Thus, through classified images, we verify how changing the properties of the input data affects their quality. The input is an actual PolSAR image, the control parameters are (i) the filter (Local Mean, LM, or Model-Based PolSAR, MBPolSAR) and the size of their support, and (ii) the classification method (Maximum Likelihood, ML, or Support Vector Machine, SVM), and the output is the precision of the classification algorithm applied to the filtered data. To expand the conclusions, this study deals not only with Classification Accuracy but also with Kappa and Overall Accuracy as measures of map precision. Experiments were conducted on two airborne PolSAR images. Differently from what was observed in previous works, almost all quality measures are good and increase with degradation, i.e. the filtering algorithms that we used always improve the classification results at least up to supports of size 7 × 7.

scholarly journals Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

2021 ◽  
Vol 13 (18) ◽  
pp. 3697
Author(s):  
Liangliang Li ◽  
Hongbing Ma ◽  
Zhenhong Jia
Keyword(s):  
Neural Networks ◽  
Change Detection ◽  
Saliency Detection ◽  
Speckle Noise ◽  
Saliency Map ◽  
Wavelet Neural Networks ◽  
Sar Images ◽  
Difference Image ◽  
The Difference ◽  
Log Ratio

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

scholarly journals Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3877 ◽  
Author(s):  
Tao Xie ◽  
Weike Zhang ◽  
Linna Yang ◽  
Qingping Wang ◽  
Jingjian Huang ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Signal To Noise Ratio ◽  
Visual Saliency ◽  
Saliency Map ◽  
Local Contrast ◽  
Signal To Noise ◽  
Sar Images ◽  
Ship Detection ◽  
Noise Ratio

Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.

scholarly journals Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Chunyan Zhang ◽  
Chen Ning ◽  
Yuzhen Zhang ◽  
Guofang Lv
Keyword(s):  
Sparse Representation ◽  
Background Noise ◽  
Saliency Detection ◽  
Infrared Image ◽  
Saliency Map ◽  
Local Contrast ◽  
Infrared Images ◽  
Saliency Maps ◽  
Contrast Measure ◽  
Local Sparse Representation

For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

scholarly journals Revisiting the effect of spatial resolution on information content based on classification results

2021 ◽  
Author(s):  
MG Palacio ◽  
SB Ferrero ◽  
Alejandro Frery
Keyword(s):  
Information Content ◽  
Classification Accuracy ◽  
Speckle Noise ◽  
Support Vector ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Local Mean ◽  
Almost All ◽  
Processing Techniques ◽  
Source Of Information

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kinds of images, and how it is affected by usual processing techniques. Previous works have resulted in various approaches for quantifying image information content. In this paper, we study this problem from the classification accuracy viewpoint, focusing on the filtering and the classification stages. Thus, through classified images, we verify how changing the properties of the input data affects their quality. The input is an actual PolSAR image, the control parameters are (i) the filter (Local Mean, LM, or Model-Based PolSAR, MBPolSAR) and the size of their support, and (ii) the classification method (Maximum Likelihood, ML, or Support Vector Machine, SVM), and the output is the precision of the classification algorithm applied to the filtered data. To expand the conclusions, this study deals not only with Classification Accuracy but also with Kappa and Overall Accuracy as measures of map precision. Experiments were conducted on two airborne PolSAR images. Differently from what was observed in previous works, almost all quality measures are good and increase with degradation, i.e. the filtering algorithms that we used always improve the classification results at least up to supports of size 7 × 7.

scholarly journals Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23071-23094 ◽  
Author(s):  
Yihua Tan ◽  
Qingyun Li ◽  
Yansheng Li ◽  
Jinwen Tian
Keyword(s):  
High Resolution ◽  
Saliency Map ◽  
Sar Images ◽  
Aircraft Detection

scholarly journals A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1295 ◽  
Author(s):  
Huifu Zhuang ◽  
Hongdong Fan ◽  
Kazhong Deng ◽  
Guobiao Yao
Keyword(s):  
Change Detection ◽  
Window Size ◽  
Speckle Noise ◽  
Adaptive Strategy ◽  
Negative Influence ◽  
Sar Images ◽  
Spatial Homogeneity ◽  
Difference Image ◽  
Negative Effect ◽  
Adaptive Neighborhood

The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.

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