監督性變換器模型對變遷偵測應用的 預訓練與微調策略;Supervised transformer-based models pre-training and fine-tunning strategies for change detection

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Title:	監督性變換器模型對變遷偵測應用的預訓練與微調策略;Supervised transformer-based models pre-training and fine-tunning strategies for change detection
Authors:	柯浩飛;Cardenas, Juan Felipe Giraldo
Contributors:	遙測科技碩士學位學程
Keywords:	變換器;Transformer
Date:	2024-07-26
Issue Date:	2024-10-09 17:13:30 (UTC+8)
Publisher:	國立中央大學
Abstract:	影像變遷偵測是遙測重要應用之一，其目的在自動偵測同一場景在不同時間拍攝的兩張或多個影像之間的變化。然而，對機器學習演算法，大多數資料庫的樣本數量都很少，導致模型產生過度擬合的問題。為了應對這項挑戰，我們使用一些遷移學習策略，將一個資料集中獲得的知識傳輸到新的訓練並進行微調，以便新模型能從兩個資料集中學習，並且可以成為能夠跨不同資料庫的模型。目前最先進的方法依賴深度學習和變換器(Transformer)架構。本研究基於變換器模型（特別是 BIT 和 ChangeFormer）在使用遷移學習檢測對不同資料庫的效能。研究目的在利用對變換器全面環境進行建模的能力來提高變遷偵測的準確性。透過在 LEVIR-CD、WHU-CD 和 DSIFN-CD 等三個資料庫上評估這些模型，包括它們在各種場景下的適應性和穩健性。評估指標包括整體準確度 (Overall Accuracy)、交並比 (Intersection-over-Union)、F1 分數、精確度和召回率。透過將知識從一個資料庫轉移到另一個資料庫的微調模型，利用指標顯示變遷偵測的改進，展示轉換器和遷移學習管道可以幫助處理變遷偵測任務的策略。;Image change detection is an important task in remote sensing, aiming to automatically detect changes between two or more images of the same scene taken at different times. However, most of the available datasets are small, leading the models to overfitting. To deal with this challenge, we used some transfer learning strategies to leverage the knowledge obtained in one dataset to be transmitted to a new training (fine-tuning), so that the new model learns from both datasets and can be generalized being able to model global context across datasets. State-of-the-art approaches rely their methods on deep learning and transformer architectures. This research investigates the effectiveness of transformer-based models, specifically the Bitemporal Image Transformer (BIT) and ChangeFormer, in detecting changes across different datasets using transfer learning. The study aims to leverage the ability to model global context of transformers to enhance change detection accuracy. By evaluating these models on datasets such as LEVIR-CD, WHU-CD, and DSIFN-CD, we assess their adaptability and robustness in various scenarios. The metrics to evaluate our pipelines are the Overall Accuracy (OA), Intersection-over-Union (IoU), F1-score, Precision and Recall. By transferring knowledge from a dataset to a fine-tuned model on another dataset, the metrics show an improvement detecting changes demonstrating that transformers and transfer learning pipelines can help to deal with change detection tasks.
Appears in Collections:	[Master of Science Program in Remote Sensing Science and Technology ] Electronic Thesis & Dissertation

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