According to statistics, more than 50% optical satellite images are covered by cloud or fog. Moreover, the cloud-cover rate is much higher in large bodies of water and nearby areas than inland areas due to the high amount of water evaporation and condensation. Therefore, ship detection from optical remote sensing images based on water surface analysis is more susceptible to cloud and fog interference, which affects the detection accuracy. In the time-sensitive application field of remote sensing images, due to the massive parameters in the large-scale network model, the detection speed is slow, and a lightweight detection model is commonly used. However, it is difficult for the lightweight detection model to achieve both high efficiency and accuracy for ship detection in a cloud-cover environment. To solve these problems, this manuscript proposes a lightweight algorithm called Fog Remote Sensing Ship Detection Network (FRS-Net) suitable for ship detection from remote sensing images in thin-cloud and fog covered environments. FRS-Net is developed based on the Deep Learning algorithm and can effectively improve ship detection accuracy under thin-cloud and fog cover. First, for the allocation strategy of anchor boxes, by using K-means clustering algorithm, FRS-Net simplifies the number of anchor boxes by utilizing the shape characteristics of the ship, which improves the time efficiency and the detection accuracy. Second, the FRS-Net network can meet the detection accuracy and has a fast inference speed. FRS-Net network is mainly composed of backbone extraction network, feature fusion network and prediction network. Experimental results on the Ship Detection in Optical Remote sensing images (SDIOR) dataset demonstrate the detection accuracy and computational efficiency of FRS-Net. The recognition mean Average Precision (mAP) achieved 43.20% for ship detection under thin-cloud and fog cover, with an efficiency of up to 424 FPS. FRS-Net has the potential to be applied in future scenarios such as embedded processing and on-board processing, where computing capabilities are strictly limited and the timeliness requirement is high.

The fast data acquisition rate due to the shorter revisit periods and wider observation coverage of satellites results in large amounts of remote sensing images every day. This brings the challenge of how to accurately search the images with similar visual content as the query image. Content-based image retrieval (CBIR) is a solution to this challenge, its performance heavily depends on the effectiveness of the image representation features and similarity evaluation metrics. Ideal image feature representations have dispersed interclass, compact intraclass distribution. However, the neural networks employed by many CBIR methods are trained with cross entropy loss, which does not directly optimize the metrics that evaluates interclass variance over intraclass variance, hence, their feature representations are suboptimal. Meanwhile, the traditional distance metrics used by many CBIR methods cannot index the similarity of feature representations well in high-dimensional space. For better CBIR performance, we propose a discriminative feature learning approach with distinguishable distance metrics for remote sensing image classification and retrieval. By balancing the diagonal elements and nondiagonal elements of the within-class scatter matrix of deep linear discriminant analysis, our proposed loss function, balanced deep linear discriminant analysis, can better optimize the Rayleigh-Ritz quotient, which measures interclass variance over intraclass variance. In addition, the proposed distance metrics, reciprocal exponential distance (RED), is more capable of maintaining distance contrast in high dimensionality, therefore, it can better index similarity for feature representations in high dimensionality. Both visual interpretations and quantitative metrics of extensive experiments demonstrated the effectiveness of our approach.