Fully Squeezed Multiscale Inference Network for Fast and Accurate Saliency Detection in Optical Remote-Sensing Images

Recently, salient object detection in optical remote-sensing images (RSIs) has received more and more attention. To tackle the challenges of RSIs including large-scale variation of objects, cluttered background, irregular shape of objects, and big difference in illumination, the cutting-edge convolutional neural network (CNN)-based models are proposed and have achieved an encouraging performance. However, the performance of the top-level models usually depends on the large model size and high computational cost, which limits their practical applications. To remedy the issue, we introduce a fully squeezed multiscale (FSM) module to equip the entire network. Specifically, the FSM module squeezes the feature maps from high dimension to low dimension and introduces the multiscale strategy to endow the capability of feature characterization with different receptive fields and different contexts. Based on the FSM module, we build the FSM inference network (FSMI-Net) to pop-out salient objects from optical RSIs, which is with fewer parameters and fast inference speed. Particularly, the proposed FSMI-Net only contains 3.6M parameters, and its GPU running speed is about 28 fps for 384 x 384 inputs, which is superior to the existing saliency models targeting optical RSIs. Extensive comparisons are performed on two public optical RSIs datasets, and our FSMI-Net achieves comparable detection accuracy when compared with the state-of-the-art models, where our model realizes a balance between the computational cast and detection performance.

Automated summary

Salient object detection in optical remote-sensing images has gained increasing attention with the proposal of cutting-edge CNN-based models. An innovative fully squeezed multiscale module has been introduced to enhance the network, achieving a balance between computational cost and detection performance.