CAJ | 학술논문

显著性目标检测是遥感图像处理的重要研究领域，传统的方法通过逐个像素点的计算来实现目标检测，难以满足遥感图像大面积实时处理的要求。将视觉注意机制应用到遥感图像的显著性目标检测中，在训练阶段，将所有的目标融合成目标类，所有的背景融合成背景类，目标类的显著性均值与背景类的显著性均值的比值得到一个权重向量；在检测阶段，所有的特征图乘以权重向量得到自顶向下的显著性图；自顶向下和自底向上的显著性图融合生成全局显著性图。实验结果表明当目标和背景不是总成对出现时，该方法的检测结果优于Navalpakkam模型和Frintrop模型的检测结果。
현저성목표검측시요감도상처리적중요연구영역，전통적방법통과축개상소점적계산래실현목표검측，난이만족요감도상대면적실시처리적요구。장시각주의궤제응용도요감도상적현저성목표검측중，재훈련계단，장소유적목표융합성목표류，소유적배경융합성배경류，목표류적현저성균치여배경류적현저성균치적비치득도일개권중향량；재검측계단，소유적특정도승이권중향량득도자정향하적현저성도；자정향하화자저향상적현저성도융합생성전국현저성도。실험결과표명당목표화배경불시총성대출현시，해방법적검측결과우우Navalpakkam모형화Frintrop모형적검측결과。
Saliency object detection is one of the important research fields in remote sensing image processing. The tradi-tional methods achieve object detection by calculating the point-by-pixel, which is difficult to meet the requirement of large-scale real-time remote sensing image processing. This paper proposes a remote sensing image object detection model based on visual attention mechanism. In the training phase, all objects are fused into an object class and all backgrounds are fused into a background class. Weight vector is calculated as the ratio of the mean object class saliency and the mean background class saliency for all the features. In the detection phase, all feature maps are combined into a top-down saliency map multiplied by the weight vector, then top-down and bottom-up saliency maps are fused into a global saliency map. Experimental results indicate that when the object and background do not always appear in pairs, this method is excellent to Navalpakkam’s model and Frintrop’s model.