While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

No abstract

Abstract. In recent years, great progress has been made in the study of semantic segmentation in the field of computer vision. The accuracy of semantic segmentation has been constantly improved, and it has been widely applied in the fields of automatic driving, medical treatment and remote sensing image classification.Semantic segmentation in all kinds of neural network structure has been optimized, according to different segmentation task put forward different loss function and different optimization algorithm to improve the accuracy of classification, such as used in the classification task more softmax cross entropy loss in the sigmoid function is used in the classification task, two different loss functions have a different impact on classification results, at the same time, the training data set imbalance can also cause the precision of classification result deviation.In the task of remote sensing image classification, it is often necessary to extract and classify a variety of different land types, such as road, water system, vegetation, etc., from an image, but sometimes it is also necessary to extract one of the land types.Due to remote sensing image contains abundant spectral information, so the remote sensing image classification task is different from ordinary classification task scenarios, common softmax and sigmoid function, the number can not meet the existing remote sensing image classification task, this requires a combination of specific classification task to adjust and optimize the loss function, to adapt to the different classification task.As a major sugarcane planting province in China, guangxi plays an important role in the development of China's sugar industry. Therefore, it is of great significance to propose sugarcane planting area through high-resolution satellite remote sensing image.But because of guangxi planting condition is complicated and changeable weather condition, often appear cloudy, so in high resolution satellite remote sensing image acquisition and there is still a big challenge on extraction and classification, and on the high rate of satellite remote sensing image texture feature of sugarcane and cassava, corn and other crops of texture feature are similar, therefore in the process of classification will easy to misjudge corn, cassava as sugar cane, which led to a decline in classification accuracy.This paper combined with the extraction of sugarcane planting area based on high-resolution satellite remote sensing images by Jaccard loss of Lovasz hinge, and compared the effects of different loss functions on the accuracy of the results through experiments. Finally, it was concluded that combining Jaccard loss of Lovasz hinge could effectively reduce losses and improve the extraction accuracy of sugarcane planting area.