Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

Salient object detection in optical remote sensing images (RSI-SOD) aims to segment
objects that attract human attention in optical RSIs. With the tremendous success of full …

In this article, we deal with the problem of object detection on remote sensing images.
Previous researchers have developed numerous deep convolutional neural network (CNN) …

Optical remote sensing images (RSIs) have been widely used in many applications, and one
of the interesting issues about optical RSIs is the salient object detection (SOD). However …

Despite the remarkable advances in visual saliency analysis for natural scene images
(NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains …

Saliency detection aims to highlight the most relevant objects in an image. Methods using
conventional models struggle whenever salient objects are pictured on top of a cluttered …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

Advanced data augmentation strategies have widely been studied to improve the
generalization ability of deep learning models. Regional dropout is one of the popular …

Deep convolutional neural networks (CNNs) have been successfully applied to a wide
variety of problems in computer vision, including salient object detection. To detect and …

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an
emerging topic in understanding optical RSIs. However, due to the difference between …