SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images

Fine-grained ship instance segmentation in satellite images holds considerable significance for monitoring maritime activities at sea. However, existing datasets often suffer from the scarcity of fine-grained information or pixel-wise localization annotations, as well as the insufficient image diversity and variations, thus limiting the research of this task. To this end, we propose a benchmark dataset for fine-grained Ship Instance Segmentation in Panchromatic satellite images, namely SISP, which contains 56,693 well-annotated ship instances with four fine-grained categories across 10,000 sliced images, and all the images are collected from SuperView-1 satellite with the resolution of 0.5m. Targets in the proposed SISP dataset have characteristics that are consistent with real satellite scenes, such as high class imbalance, various scenes, large variations in target densities and scales, and high inter-class similarity and intra-class diversity, all of which make the SISP dataset more suitable for real-world applications. In addition, we introduce a Dynamic Feature Refinement-assist Instance segmentation network, namely DFRInst, as the benchmark method for ship instance segmentation in satellite images, which can fortify the explicit representation of crucial features, thus improving the performance of ship instance segmentation. Experiments and analysis are performed on the proposed SISP dataset to evaluate the benchmark method and several state-of-the-art methods to establish baselines for facilitating future research. The proposed dataset and source codes will be available at: https://github.com/Justlovesmile/SISP.

EARL: An Elliptical Distribution aided Adaptive Rotation Label Assignment for Oriented Object Detection in Remote Sensing Images

Label assignment is often employed in recent convolutional neural network (CNN) based detectors to determine positive or negative samples during training process. However, we note that current label assignment strategies barely consider the characteristics of targets in remote sensing images thoroughly, such as large variations in orientations, aspect ratios and scales, which lead to insufficient sampling. In this paper, an Elliptical Distribution aided Adaptive Rotation Label Assignment (EARL) is proposed to select positive samples with higher quality in orientation detectors, and yields better performance. Concretely, to avoid inadequate sampling of targets with extreme scales, an adaptive scale sampling (ADS) strategy is proposed to dynamically select samples on different feature levels according to the scales of targets. To enhance ADS, positive samples are selected following a dynamic elliptical distribution (DED), which can further exploit the orientation and shape properties of targets. Moreover, a spatial distance weighting (SDW) module is introduced to mitigate the influence from low-quality samples on detection performance. Extensive experiments on popular remote sensing datasets, such as DOTA and HRSC2016, demonstrate the effectiveness and the superiority of our proposed EARL, where without bells and whistles, it achieves 72.87 of mAP on DOTA dataset by being integrated with simple structure, which outperforms current state-of-the-art anchor-free detectors and provides comparable performance as anchor-based methods. The source code will be available at https://github.com/Justlovesmile/EARL