SAR Data Fusion Using Nonlinear Principal Component Analysis

Fasano, Luca; Latini, Daniele; Machidon, Alina L.; Clementini, Chiara; Schiavon, G.; Frate, Fabio Del

doi:10.1109/lgrs.2019.2951292

Cited by 8 publications

(16 citation statements)

References 13 publications

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“…Experimental analyses were conducted by comparing the proposed method with several commonly used image fusion approaches and conventional processing methods using single-SAR images. These comparative image fusion approaches included existing additive and multiplicative fusion approaches [10], PCA [12], DWT [13], fuzzy logic [16], and AANN-based fusion [18]. For more implementation details, we refer the reader to [10][11][12][13]16,18].…”

Section: Resultsmentioning

confidence: 99%

“…These comparative image fusion approaches included existing additive and multiplicative fusion approaches [10], PCA [12], DWT [13], fuzzy logic [16], and AANN-based fusion [18]. For more implementation details, we refer the reader to [10][11][12][13]16,18]. For the single-SAR image processing cases, the spaceborne SAR images were used directly; while, for the airborne SAR images, the horizontal and vertical polarization versions were averaged simply and then used in the following comparison.…”

Section: Resultsmentioning

confidence: 99%

“…The crucial problem is to attain a fused image with enhanced target regions and suppressed backgrounds from the spaceborne and airborne SAR images, in order to increase the target-to-clutter ratio (TCR) and improve the vessel target detection performance. Considering the issue of image fusion, some works have been carried out in the existing literature [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The most straightforward and computationally efficient fusion methods are arithmetic-based fusion approaches, such as addition fusion and multiplication fusion, but this class of fusion approaches might maintain a high level of background clutter, resulting in false alarms, or excessively suppress relatively weak targets, resulting in missed detections [10].…”

Section: Related Workmentioning

confidence: 99%

“…In [17], a framelet domain-based random walk approach is proposed for multi-band SAR image fusion, by which the target contrast is enhanced and edges are well-preserved. In [18], a non-linear PCA approach based on an auto-associative neural network (AANN) was presented to fuse SAR images from different spaceborne platforms and improve the target classification performance. In [19], a fusion method was proposed to combine the strengths of hyperspectral and multispectral images, in order to achieve spatial resolution enhancement.…”

Section: Related Workmentioning

confidence: 99%

“…Figures 6-8 illustrate the fused images obtained by additive fusion, multiplicative fusion, PCA fusion, DWT fusion, fuzzy logic, AANN fusion, and the proposed TPPIE method, as well as the proposed ITSPM fusion method (which combines the NG-based TP and edge-based morphological candidate map). [10]; (d) multiplicative fusion [10]; (e) PCA fusion [12]; (f) DWT fusion [13]; (g) fuzzy fusion [16]; (h) AANN fusion [18]; (i) proposed ITSPM fusion; and (j) proposed TPPIE fusion. The proposed methods can achieve better fused images with enhanced target regions and suppressed background regions, compared to the other image fusion approaches.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

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Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

et al. 2021

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This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.

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