The ship detection technology based on visual saliency methods is a research hotspot in the field of remote sensing. Aiming at the application of visual saliency methods under complex sea conditions, this paper firstly introduces the target characteristics of visible bands of the optical remote sensing image, and then analyzes the advantages and disadvantages of typical visual saliency methods applied to maritime ship detection through experiments. Finally, the problems of existing visual saliency methods applied to ship detection are summarized.
Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.