Affiliation:
1. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
2. College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
Abstract
Instance segmentation (IS) of remote sensing (RS) images can not only determine object location at the box-level but also provide instance masks at the pixel-level. It plays an important role in many fields, such as ocean monitoring, urban management, and resource planning. Compared with natural images, RS images usually pose many challenges, such as background clutter, significant changes in object size, and complex instance shapes. To this end, we propose a query-based RS image cascade IS network (QCIS-Net). The network mainly includes key components, such as the efficient feature extraction (EFE) module, multistage cascade task (MSCT) head, and joint loss function, which can characterize the location and visual information of instances in RS images through efficient queries. Among them, the EFE module combines global information from the Transformer architecture to solve the problem of long-term dependencies in visual space. The MSCT head uses a dynamic convolution kernel based on the query representation to focus on the region of interest, which facilitates the association between detection and segmentation tasks through a multistage structural design that benefits both tasks. The elaborately designed joint loss function and the use of the transfer-learning technique based on a well-known dataset (MS COCO) can guide the QCIS-Net in training and generating the final instance mask. Experimental results show that the well-designed components of the proposed method have a positive impact on the RS image instance segmentation task. It achieves mask average precision (AP) values of 75.2% and 73.3% on the SAR ship detection dataset (SSDD) and Northwestern Polytechnical University Very-High-Resolution dataset (NWPU-VHR-10 dataset), outperforming the other competitive models. The method proposed in this paper can enhance the practical application efficiency of RS images.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference55 articles.
1. Amitrano, D., Di Martino, G., Guida, R., Iervolino, P., Iodice, A., Papa, M.N., Riccio, D., and Ruello, G. (2021). Earth environmental monitoring using multi-temporal synthetic aperture radar: A critical review of selected applications. Remote Sens., 13.
2. Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects;Liu;Earth-Sci. Rev.,2022
3. Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China;Chen;ISPRS J. Photogramm. Remote Sens.,2023
4. Multiscale U-shaped CNN building instance extraction framework with edge constraint for high-spatial-resolution remote sensing imagery;Liu;IEEE Trans. Geosci. Remote Sens.,2020
5. Hyperspectral image instance segmentation using spectral–spatial feature pyramid network;Fang;IEEE Trans. Geosci. Remote Sens.,2023