Insight Any Instance: Promptable Instance Segmentation for Remote Sensing Images

doi:10.1109/TGRS.2025.3543636

Insight Any Instance: Promptable Instance Segmentation for Remote Sensing Images

2025-01-01

0

OA

AI

DOI：10.1109/TGRS.2025.3543636

原文链接

原文求助

摘要

En 中文

Instance segmentation of remote sensing images (RSIs) is an essential task for a wide range of applications such as land planning and intelligent transport. Instance segmentation of RSIs is constantly plagued by the unbalanced ratio of foreground and background and limited instance size. And most of the instance segmentation models are based on deep feature learning and contain operations such as multiple downsampling, which is harmful to instance segmentation of RSIs, and thus the performance is still limited. Inspired by the recent superior performance of prompt learning in visual tasks, we propose a new prompt paradigm to address the above issues. Based on the existing instance segmentation model, first, a local prompt module is designed to mine local prompt information from original local tokens for specific instances; second, a global-to-local prompt module is designed to model the contextual information from the global tokens to the local tokens where the instances are located for specific instances. Finally, a proposal's area loss function (PAreaLoss) is designed to add a decoupling dimension for proposals on the scale to better exploit the potential of the above two prompt modules. It is worth mentioning that our proposed approach can extend the instance segmentation model to a promptable instance segmentation model, i.e., to segment the instances with the specific boxes' prompt. The time consumption for each promptable instance segmentation process is only 40 ms. This article evaluates the effectiveness of our proposed approach based on several existing models in four instance segmentation datasets of RSIs, and thorough experiments prove that our proposed approach is effective for addressing the above issues and is a competitive model for instance segmentation of RSIs.

Keyword:

Instance segmentation

Remote sensing

Feature extraction

Transformers

Representation learning

Context modeling

Computational modeling

Visualization

Proposals

Data models

Global-to-local

instance segmentation

prompt

remote sensing

Insight Any Instance: Promptable Instance Segmentation for Remote Sensing Images

摘要

期刊

机构

引用论文