LAUSR

Saliency analysis is an essential task in computer vision and aims to generate distinguishing foreground features from background features. However, due to complex structure distributions in large-scale optical remote sensing scenes, generating effective feature descriptions for regional saliency analysis is challenging. Therefore, in this study, we proposed a novel global–local feature assembly method called GLFA-Net based on a convolution neural network (CNN) that can adaptively learn an effective feature representation for regional saliency analysis to achieve the region-of-interest (ROI) (e.g., aircraft carrier, airport, and urban area) extraction from complex optical remote sensing images. In addition, we also collected these complex optical remote sensing scene images from Google Earth and DOTA data sets to demonstrate the effectiveness of the proposed method. Finally, experimentation shows that the proposed regional saliency analysis method can produce better ROI extraction performance than other methods, reaching a 0.057 mean absolute error (MAE), a 0.703 Kappa coefficient, and a 0.735 F1-score.