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| Title: | 基於深度學習之衛星圖像變遷偵測優化
Optimization of Deep Learning-based Change Detection in Satellite Images |
| Authors: | 陳湘淇
Chen, Hsiang-Chi |
| Contributors: | 廖文宏
Liao, Wen-Hung
陳湘淇
Chen, Hsiang-Chi |
| Keywords: | 深度學習
卷積神經網路
轉換器
衛星影像
地景變遷偵測
Deep learning
Convolutional neural networks
Transformer
Satellite images
Change detection |
| Date: | 2022 |
| Issue Date: | 2022-10-05 09:14:53 (UTC+8) |
| Abstract: | 地景變遷偵測為遙測影像分析的基本應用之一，該任務須自給定之兩張同一地區、不同時間點之衛星影像，偵測出變遷部位，廣泛被運用於環境監控、災害評估、土地資源規劃等範疇。深度學習引入地景變遷偵測任務，能夠輔助資料標註人員加速工作流程；近幾年，除了在電腦視覺領域發展越趨成熟的卷積神經網路，基於轉換器的視覺任務架構大放異彩，本研究分別選用基於卷積網路、純轉換器、混合結構作為編碼的SNUNet、ChangeFormer與BIT地景變遷偵測模型進行探討，針對不同條件評估模型影響，並以此優化偵測表現。
為維持模型面對不同變遷性質，或來自不同資料集之樣本的適應能力，本研究從訓練資料方面調整，增加一倍輸入時序交換的資料量或合併資料集進行訓練；另外我們也從目標函數端修改提出雙向損失，在不更動資料集之情況下，讓模型同時學習到「出現、消失」類型之變遷。上述訓練方式皆能有效提升模型泛化能力，在LEVIR-CD測試集上，IoU-1自不及0.1上升至超越0.7，達到接近基準之表現（0.7631）；在S2Looking測試集上超越基準（0.4184），從小於0.1的IoU-1提升到0.4422。
Change detection (CD), one of the fundamental applications in remote sensing (RS) image analysis, aims to identify surface changes based on bitemporal images of the same area. It is widely used in environmental monitoring, disaster assessment and land resource planning. Introducing deep learning approaches for change detection could help geographic data annotation workers improve workflow efficiency. In addition to convolutional neural network (CNN), the deep learning framework that has achieved remarkable performance on a variety of computer vision applications in recent years is transformer. To compare and improve the performance of change detection, this research investigates modern change detection models, namely, SNUNet, ChangeFormer and BIT, which are CNN-based, pure transformer-based and CNN-transformer hybrid encoding model, respectively.
In this work, we attempt to maintain the adaptability of the CD model when processing input image pairs which have different changed types or are from another datasets. In terms of training data, we can either double the number of training pairs d by adding the same bitemporal images in reverse order or merge CD datasets to build a larger training data. In terms of objective function, we propose a bidirectional loss, which considers not only newly built but also demolished areas without the need for data augmentation. Experimental results show that the above approaches attain significant accuracy improvements (over 0.7 from less than 0.1 of the IoU-1 on the LEVIR-CD test sets; from below 0.1 of the IoU-1 increased to 0.4422 on the S2Looking test sets) and greatly enhance the model’s generalization capability. |
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| Description: | 碩士
國立政治大學
資訊科學系
109753114 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109753114 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201612 |
| Appears in Collections: | [資訊科學系] 學位論文
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