應用地球觀測的組合式影像檢索基準測試

摘要

遙感影像組合檢索（RSCIR）可透過結合參考影像與文字修飾語的組合查詢，在大型衛星影像資料庫中進行搜尋。儘管RSCIR提供了一個靈活的介面來表達特定檢索意圖，但現代組合方法對地球觀測（EO）影像的可遷移性及其與實際EO工作流程的相關性，仍尚未得到充分探索。為解決此問題，我們建立了一個統一的基準測試與應用導向研究。首先，我們系統性地調整與評估六種視覺-語言主幹網路下的代表性組合影像檢索方法，並在標準化協議下於PatternCom資料集上進行分析，探討其在不同主幹網路、組合策略及查詢類型中的表現。其次，我們引入了xView2-CIR，這是一個以變化為核心的災害與損害監測資料集，其檢索條件取決於場景身分與目標災後狀態。結果顯示，無須訓練的組合方法能為EO檢索提供強大且可擴展的基準，而以變化為核心的檢索則面臨與屬性檢索不同的挑戰，特別是在需要保留場景身分的需求上。整體而言，本研究為RSCIR建立了實用基準，並將組合檢視為遙感影像檢索、資料庫探索與變化分析的輔助工具。資料集與程式碼可於https://github.com/billpsomas/rscir取得。

English

Remote sensing composed image retrieval (RSCIR) enables search in large satellite image archives using composed queries that combine a reference image with a textual modifier. Although RSCIR offers a flexible interface for expressing targeted retrieval intent, the transferability of modern composition methods to Earth observation (EO) imagery and their relevance to operational EO workflows remain underexplored. We address this gap through a unified benchmark and an application-oriented study. First, we systematically adapt and evaluate representative composed image retrieval methods with six vision-language backbones on PatternCom under a standardized protocol, analyzing their behavior across backbones, composition strategies, and query types. Second, we introduce xView2-CIR, a change-centric dataset for disaster and damage monitoring, where retrieval is conditioned on scene identity and a target post-event state. Our results show that training-free composition methods provide strong and scalable baselines for EO retrieval, while change-centric retrieval presents different challenges from attribute-based retrieval, particularly due to the need to preserve scene identity. Overall, this study establishes a practical benchmark for RSCIR and positions composed retrieval as a complementary tool for remote sensing image retrieval, archive exploration, and change analysis. The dataset and code are available at https://github.com/billpsomas/rscir.