Selective routing improves detecting and describing changes in satellite images
CoDeRNet: Selective Cross-Task Routing under Heterogeneous Supervision for Change Detection and Captioning
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Detecting and describing changes between two satellite images is tricky because finding where things changed and explaining those changes require different kinds of information. The authors came up with a new system called CoDeRNet that helps the computer decide when and where to share information between these two tasks. This system improves the quality of captions explaining changes without losing accuracy in finding those changed areas. The key idea is a special routing method that carefully controls the flow of information based on needs and confidence.
What this means in practice
- •For remote sensing analysts: Generate more accurate descriptions of landscape changes while keeping precise change locations in satellite images.
- •For environmental monitoring teams: Improve automated tracking and explaining of environmental changes from satellite data to support decision making.
Authors
Eunki Cho, Hyeon Bae Kim, Seong Tae Kim
Abstract
Change Detection & Captioning (CDC) aims to jointly localize changed regions and describe their semantic content from bi-temporal remote sensing images. While recent approaches have demonstrated the effectiveness of unified modeling, they primarily rely on shared representations or jointly optimized architectures, leaving task-specific communication under heterogeneous supervision less explored. However, detection and captioning differ fundamentally in their supervision granularity, leading to heterogeneous representations with uneven reliability across tasks and spatial locations. In this paper, we reformulate CDC as a selective information routing problem between heterogeneous task branches, where the key challenge is to determine when and where information should be transferred. To this end, we propose CoDeRNet, a unified framework that preserves task-specific representations while enabling bidirectional exchange of complementary information. CoDeRNet introduces a Confidence-Decomposed Routing (CoDeR) mechanism, which decomposes the routing condition into receiver-side need for support and sender-side reliability cues to guide selective transfer. Experiments on LEVIR-MCI and WHU-CDC show that CoDeRNet achieves strong overall CDC performance, particularly improving caption generation while preserving competitive change localization. Extensive analyses further show that the benefits arise not merely from joint learning or generic cross-task communication, but from explicitly modeling selective cross-task information routing. These results highlight the importance of conditional information transfer for unified CDC under heterogeneous supervision.