ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation

2026-08-03

AI Summary

This work addresses the limitation of existing interactive segmentation methods for remote sensing imagery, which treat all instances of the same class as a single object, preventing click sharing across distinct instances and necessitating excessive user interactions. To overcome this, the authors propose a detection-guided interactive segmentation framework that, for the first time, incorporates object-level detection cues during both training and inference. By leveraging strong inter-instance correlations among same-class objects, the method integrates an RF-DETR decoder with the segmentation backbone and introduces a dynamic Top-K click selection mechanism to convert high-confidence detection proposals into simulated clicks. This enables a single user click to effectively segment multiple instances of the same class. Evaluated on three remote sensing benchmarks, the approach achieves substantial gains in accuracy while significantly reducing the number of clicks per image (NoC-I), thereby enhancing practical usability.

Abstract

Interactive segmentation reduces the prohibitive cost of pixel-level annotation by allowing users to delineate objects with a few clicks. However, applying this paradigm directly to remote sensing imagery is non-trivial: ultra-high resolutions, small object sizes, and sparse spatial distributions all degrade segmentation quality. Recent work has addressed the resolution barrier and achieved competitive results in interactive segmentation for remote sensing (ISRS). However, they treat all instances of a class within an image as a single objective target. Consequently, interactions spent on one object contribute nothing to its same-class neighbours, and satisfactory masks may demand up to 40 clicks per image, hindering the practicality of these frameworks. We observe that remote sensing scenes exhibit markedly strong inter-object correlation, meaning a single clicked object is highly informative about the rest of its category. Building on this, we propose ISRS-DETR, a detection-guided interactive segmentation framework that injects object-level evidence into both training and inference. Our ISRS-DETR employs an RF-DETR decoder with the interactive segmentation backbone to localise co-occurring same-class objects, and introduces a Dynamic Top-K Click Selection strategy that retains only reliable proposals and converts each into a simulated click, so one user interaction propagates across an entire class. Experiments on three standard remote sensing benchmarks show that ISRS-DETR achieves state-of-the-art accuracy while substantially reducing Number of Clicks per Image (NoC-I). All codes and data splits will be released for reproducibility upon acceptance.

Problem

• interactive segmentation
• remote sensing
• click propagation
• object correlation
• instance-level segmentation

Innovation

• interactive segmentation
• remote sensing
• click propagation
• detection-guided
• object correlation