自適應(yīng)閾值的視覺注意模型SAR艦船檢測(cè)算法
發(fā)布時(shí)間：2018-11-22 08:28
【摘要】：為了解決SAR圖像基于人類視覺注意模型艦船檢測(cè)算法中需要人工確定經(jīng)驗(yàn)閾值的問題,提出一種自適應(yīng)閾值的視覺注意模型SAR艦船檢測(cè)算法。引入最大類間方差(OTSU)法確定自適應(yīng)閾值進(jìn)行圖像初分割,再應(yīng)用視覺注意模型得到視覺顯著圖,最終根據(jù)顯著圖的統(tǒng)計(jì)特性進(jìn)行自適應(yīng)閾值分割檢測(cè)出艦船目標(biāo)。該算法相對(duì)于已有的視覺注意模型艦船檢測(cè)算法自動(dòng)化程度更高,與視覺注意模型艦船檢測(cè)算法以及目前普遍使用的雙參數(shù)CFAR、K-CFAR、KSW雙閾值算法同時(shí)處理3種星載SAR數(shù)據(jù)——ENVISAT ASAR(25 m)、Sentinel-1(10m)和Cosmo-SkyMed(3m),進(jìn)行對(duì)比分析實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果證明該算法簡單、準(zhǔn)確、高效。
[Abstract]:In order to solve the problem of manually determining the empirical threshold in the ship detection algorithm of SAR image based on human visual attention model, a visual attention model SAR ship detection algorithm based on adaptive threshold is proposed. The maximum inter-class variance (OTSU) method is introduced to determine the adaptive threshold for initial image segmentation, and then the visual salient map is obtained by using visual attention model. Finally, according to the statistical characteristics of the salience map, the adaptive threshold segmentation is carried out to detect the ship target. Compared with the existing visual attention model ship detection algorithm, this algorithm has a higher degree of automation, compared with the visual attention model ship detection algorithm and the widely used two-parameter CFAR,K-CFAR,. Three kinds of spaceborne SAR data, ENVISAT ASAR (25 m), Sentinel-1 (10m) and Cosmo-SkyMed (3m), are processed simultaneously by KSW double threshold algorithm. The experimental results show that the algorithm is simple, accurate and efficient.
【作者單位】： 山東科技大學(xué)測(cè)繪科學(xué)與工程學(xué)院;中國測(cè)繪科學(xué)研究院;
【基金】：國家基礎(chǔ)測(cè)繪科技計(jì)劃(2016KJ0103) 中國博士后科學(xué)基金資助項(xiàng)目(2016M591219)
【分類號(hào)】：TN957.52
本文編號(hào)：2348694
[Abstract]:In order to solve the problem of manually determining the empirical threshold in the ship detection algorithm of SAR image based on human visual attention model, a visual attention model SAR ship detection algorithm based on adaptive threshold is proposed. The maximum inter-class variance (OTSU) method is introduced to determine the adaptive threshold for initial image segmentation, and then the visual salient map is obtained by using visual attention model. Finally, according to the statistical characteristics of the salience map, the adaptive threshold segmentation is carried out to detect the ship target. Compared with the existing visual attention model ship detection algorithm, this algorithm has a higher degree of automation, compared with the visual attention model ship detection algorithm and the widely used two-parameter CFAR,K-CFAR,. Three kinds of spaceborne SAR data, ENVISAT ASAR (25 m), Sentinel-1 (10m) and Cosmo-SkyMed (3m), are processed simultaneously by KSW double threshold algorithm. The experimental results show that the algorithm is simple, accurate and efficient.
【作者單位】： 山東科技大學(xué)測(cè)繪科學(xué)與工程學(xué)院;中國測(cè)繪科學(xué)研究院;
【基金】：國家基礎(chǔ)測(cè)繪科技計(jì)劃(2016KJ0103) 中國博士后科學(xué)基金資助項(xiàng)目(2016M591219)
【分類號(hào)】：TN957.52
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