光學衛星影像輻射正規化是多時序與跨感測器遙測分析中關鍵的預處理步驟，因為多時期影像輻射不一致性會嚴重影響時間序列的判讀與變遷偵測。雖然現有的正規化方法—從統計和基於影像的技術，到典型相關分析和迭代加權多元變化檢測等基於特徵的方法—在減少輻射差異方面已展現成效，但它們往往受限於成對假設以及缺乏對季節變異性的考量。當應用於長期的多感測器資料集時，這些限制可能導致正規化結果的不平衡。本研究提出了一種季節性鬆弛輻射正規化框架，整合了基於鬆弛的正規化與季節感知圖結構，在改善時間一致性的同時，保留具意義的季節性動態。該框架將多時序影像組織為季節性群組，並明確納入季節間的過渡影像，從而實現包含跨季節邊界的季間正規化（inter-normalization）與季節群組內的季內正規化（intra-normalization）之兩階段策略。此外，本研究進一步採用相似度加權鬆弛機制來自適應地結合多重正規化關係，降低過度正規化（over-normalization）與正規化不足（under-normalization）的風險。
本研究利用來自熱帶與亞熱帶地區的多年期、多感測器資料集對此框架進行評估。透過包含偽不變特徵分析、目視檢查、誤差精度指標、結構相似性評估以及基於 NDVI 的時空分析等綜合評估，結果顯示此季節性鬆弛方法在獲得優異輻射一致性的同時，也能保留真實的季節變異性。研究結果證實，將季節性脈絡納入基於鬆弛的正規化中，能為使用異質衛星影像進行長期環境監測提供一套穩健且易於解釋的解決方案。

Radiometric normalization is a crucial preprocessing step for multi-temporal and cross-sensor remote sensing analysis, as radiometric inconsistencies can significantly impair time series interpretation and change detection. Although existing normalization methods, ranging from statistical and image-based techniques to feature-based approaches such as Canonical Correlation Analysis (CCA) and Iteratively Reweighted Multivariate Change Detection (IR-MAD), they have demonstrated effectiveness in reducing radiometric differences, they are often limited by pairwise assumptions and lack of consideration of seasonal variability. These limitations can lead to imbalanced normalization results when applied to long-term multi-sensor datasets.
This research proposes a Season-aware Relaxation-Based Radiometric Normalization framework that integrates Relaxation-Based Normalization with a season-aware graph structure to improve temporal consistency while preserving meaningful seasonal dynamics. The framework organizes multi-temporal images into seasonal groups and explicitly transition images between seasons, enabling a two-stage normalization strategy consisting of inter-normalization across seasonal boundaries and intra-normalization within seasonal groups. A similarity-weighted relaxation mechanism is further employed to adaptively combine multiple normalization relationships, reducing the risk of both under- and over-normalization.
The proposed framework is evaluated using a multi-year, multi-sensor dataset from tropical and subtropical regions. A comprehensive assessment, including pseudo-invariant feature analysis, visual inspection, error-based accuracy metrics, structural similarity evaluation, and NDVI-based spatial and temporal analysis, demonstrates that the Season-aware Relaxation-based approach achieves superior radiometric consistency while preserving true seasonal variability. The results confirm that incorporating seasonal context into Relaxation-Based Normalization provides a robust and easily interpretable solution for long-term environmental monitoring using heterogeneous satellite imagery.

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