本校學位論文庫
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論文詳情
贡澄莹
郭永德
數據科學學院
數據科學碩士學位課程(中文學制)
碩士
2023
基於Transformer網路的遙感圖像超解析度研究
Research on the super-resolution of remote sensing images based on Transformer network
遙感圖像超分辨率 ; Transformer ; 稀疏表示 ; 深度卷積神經網絡 ; 亞像素空間
Remote sensing image super-resolution(SR) ; transformer; sparse representation ; convolutional neural networks(CNNs) ; sub-pixel space
遙感圖像在土地檢測、消防管控、軍事偵查等領域逐步展示了越來越重要的作用。其中空間分辨率是遙感圖像的一個重要指標，對各類應用都有著顯著影響。基於深度神經網絡的超分辨率實現相較于提升遙感成像器件可以實現更加便捷地部署提升效果。並且隨著深度神經網絡技術的發展，超分辨率的效果逐步達到了驚人的生成質量，已成為了提升圖像分辨率的有效手段。然而相較于一般自然圖像而言，遙感圖像超分辨率處理上往往受限於遙感圖像特有的地物關係複雜以及數據量繁雜冗餘的挑戰。Transformer最近在各種視覺任務上取得了重大突破，它有望能更好地獲取遙感圖像內部相似性的全域特徵。但現有的方法往往忽略了卷積和Transformer塊之間相互作用的優化，這顯著限制了在遙感超分辨率任務上的效果。並且現有技術通常通過使用各種注意力模塊的組合來採用有限的優化方法，這限制了特徵選擇，從而損害了超分辨率性能。Transformer自注意力機制帶來全域視野的同時會帶來相當的計算成本問題。直接使用更多的注意力機制會為整體網絡的實際部署帶來挑戰。
針對以上的現有問題與挑戰，本文旨在設計一種更輕量的卷積與Transformer融合的算法，並考慮對於自注意力進行改進。基於這一研究目的，我們提出了提出了一個稀疏激活的亞像素空間Transformer網絡（Sparse-activated Sub-pixel Transformer Network，SSTNet），並考慮了自注意力計算中的稀疏編碼理論，以提高網絡生成性能。該網絡通過引入稀疏表徵對自注意力編碼過程進行調整，並基於亞像素空間進行卷積與Transformer多級融合的解碼器構建。同時使用參數共享的思想對於解碼器計算成本進行精簡。對於所提出的網絡在UCMerced數據集和AID數據集這兩個主流的遙感圖像數據集上進行2倍、3倍和4倍的超分辨率重建測試實驗，並使用PSNR和SSIM兩個評價指標進行生成效果的定量分析。在UCMerced數據集和AID數據集上，所提出的方法相較於與幾種最先進的現有方法獲得了具有競爭力的生成評價指標，並且在視覺效果上擁有更好的生成輪廓效果。在數據集的圖像類別分析上，所提出的方法可以在3倍超分辨率實驗對比下在UCMerced數據集的8個類別取得最優，在AID數據集上取得所有類別的最優，顯示了所提出策略的有效穩定提升生成的效果。研究進一步使用消融研究對於所提出的策略進行對比，結果顯示出所提出方法的有效性與可靠性。
針對以上的現有問題與挑戰，本文旨在設計一種更輕量的卷積與Transformer融合的算法，並考慮對於自注意力進行改進。基於這一研究目的，我們提出了提出了一個稀疏激活的亞像素空間Transformer網絡（Sparse-activated Sub-pixel Transformer Network，SSTNet），並考慮了自注意力計算中的稀疏編碼理論，以提高網絡生成性能。該網絡通過引入稀疏表徵對自注意力編碼過程進行調整，並基於亞像素空間進行卷積與Transformer多級融合的解碼器構建。同時使用參數共享的思想對於解碼器計算成本進行精簡。對於所提出的網絡在UCMerced數據集和AID數據集這兩個主流的遙感圖像數據集上進行2倍、3倍和4倍的超分辨率重建測試實驗，並使用PSNR和SSIM兩個評價指標進行生成效果的定量分析。在UCMerced數據集和AID數據集上，所提出的方法相較於與幾種最先進的現有方法獲得了具有競爭力的生成評價指標，並且在視覺效果上擁有更好的生成輪廓效果。在數據集的圖像類別分析上，所提出的方法可以在3倍超分辨率實驗對比下在UCMerced數據集的8個類別取得最優，在AID數據集上取得所有類別的最優，顯示了所提出策略的有效穩定提升生成的效果。研究進一步使用消融研究對於所提出的策略進行對比，結果顯示出所提出方法的有效性與可靠性。
Remote sensing images have gradually shown an increasingly important role in land detection, fire control, military reconnaissance and other fields. Among them, spatial resolution is an important indicator of remote sensing images, which has a significant impact on various applications. The super-resolution implementation based on deep neural network can achieve a more convenient deployment and improvement effect than the improvement of remote sensing imaging devices. With the development of deep neural network technology, the effect of super-resolution has gradually reached an amazing generation quality, which has become an effective means to improve image resolution. However, compared with general natural images, the super-resolution processing of remote sensing images is often limited by the complex relationship between ground objects and the challenges of complex data volume and redundancy unique to remote sensing images. Transformer has recently made major breakthroughs in various visual tasks, and it is expected to better obtain the global characteristics of the internal similarity of remote sensing images. However, the existing methods often ignore the optimization of the interaction between convolution and Transformer blocks, which significantly limits the effect on remote sensing super-resolution tasks. Although the basic attention module strengthens the feature selection ability, it is still weak in generating superior quality output. The Transformer self-attention mechanism brings a global view but also brings considerable computational costs. Using more attention mechanisms directly creates challenges for real-world deployments of the overall network.
In view of the above existing problems and challenges, this paper aims to design a lightweight convolutional and Transformer fusion algorithm, and consider improving the self-attention. Based on this research purpose, we propose a sparse-activated sub-pixel transformer network (SSTNet), and consider the sparse coding theory in self-attention computing to improve the generative performance of the network. The network adjusts the self-attention coding process by introducing sparse representation, and constructs a decoder based on subpixel space for convolution and Transformer multi-level fusion. At the same time, the idea of parameter sharing is used to streamline the computational cost of the decoder. For the proposed network, 2x, 3x and 4x super-resolution reconstruction test experiments were carried out on the two mainstream remote sensing image datasets, the UC Merced dataset and the AID dataset, and the two evaluation indexes of PSNR and SSIM were used to quantitatively analyze the generation effect. On the UC Merced dataset and the AID dataset, the proposed method obtains competitive generative evaluation indexes compared with several state-of-the-art existing methods, and has a better visual effect on generating contours. In terms of image category analysis of the dataset, the proposed method can achieve the best results in 8 categories of the UC Merced dataset and the optimal results of all categories in the AID dataset under the comparison of 3x super-resolution experiments, which shows the effective and stable improvement effect of the proposed strategy. Ablation studies were further used to compare the proposed strategies, and the results showed the effectiveness and reliability of the proposed methods.
In view of the above existing problems and challenges, this paper aims to design a lightweight convolutional and Transformer fusion algorithm, and consider improving the self-attention. Based on this research purpose, we propose a sparse-activated sub-pixel transformer network (SSTNet), and consider the sparse coding theory in self-attention computing to improve the generative performance of the network. The network adjusts the self-attention coding process by introducing sparse representation, and constructs a decoder based on subpixel space for convolution and Transformer multi-level fusion. At the same time, the idea of parameter sharing is used to streamline the computational cost of the decoder. For the proposed network, 2x, 3x and 4x super-resolution reconstruction test experiments were carried out on the two mainstream remote sensing image datasets, the UC Merced dataset and the AID dataset, and the two evaluation indexes of PSNR and SSIM were used to quantitatively analyze the generation effect. On the UC Merced dataset and the AID dataset, the proposed method obtains competitive generative evaluation indexes compared with several state-of-the-art existing methods, and has a better visual effect on generating contours. In terms of image category analysis of the dataset, the proposed method can achieve the best results in 8 categories of the UC Merced dataset and the optimal results of all categories in the AID dataset under the comparison of 3x super-resolution experiments, which shows the effective and stable improvement effect of the proposed strategy. Ablation studies were further used to compare the proposed strategies, and the results showed the effectiveness and reliability of the proposed methods.
2024
中文
48
致謝 IV
摘要 IV
Abstract V
图目录 X
表目录 XI
第1章緒論 1
1.1研究背景及意義 1
1.2國內外研究現狀 2
1.2.1基於深度學習的圖像超分辨率方法 2
1.2.2基於深度學習的遙感圖像超分辨率方法 6
1.3研究目標及思路 8
1.4本文結構安排 10
第2章相關技術概述 11
2.1圖像超分辨率概念 11
2.2卷積神經網絡 11
2.3 Transformer網絡 14
2.4亞像素空間 15
2.5稀疏表示 16
2.6基於深度學習的圖像超分辨率重建方法的框架分類 17
2.6.1預上採樣框架 17
2.6.2後上採樣框架 18
2.6.3交替上下採樣框架 18
2.6.4漸進式上採樣框架 19
2.7本章小結 20
第3章基於Transformer的遙感圖像超分辨率網絡 21
3.1研究動機 21
3.2總體網絡結構 22
3.3稀疏激活的自注意力編碼器 24
3.4 亞像素空間的多級融合解碼器 27
3.5 本章小結 28
第4章 實驗結果與分析 29
4.1 實驗環境及參數設置 29
4.2 實驗數據集 29
4.3 遙感圖像的重建質量評價方法 30
4.3.1 峰值信噪比 30
4.3.2 結構相似性 31
4.4 UCMerced 數據集上的超分辨率實驗 31
4.5 AID數據集上的超分辨率實驗 34
4.6 對網絡模型的討論與分析 37
4.7 本章小結 39
第5章 總結與展望 40
5.1 工作總結 40
5.2 研究展望 40
参考文献 42
作者簡歷 48
摘要 IV
Abstract V
图目录 X
表目录 XI
第1章緒論 1
1.1研究背景及意義 1
1.2國內外研究現狀 2
1.2.1基於深度學習的圖像超分辨率方法 2
1.2.2基於深度學習的遙感圖像超分辨率方法 6
1.3研究目標及思路 8
1.4本文結構安排 10
第2章相關技術概述 11
2.1圖像超分辨率概念 11
2.2卷積神經網絡 11
2.3 Transformer網絡 14
2.4亞像素空間 15
2.5稀疏表示 16
2.6基於深度學習的圖像超分辨率重建方法的框架分類 17
2.6.1預上採樣框架 17
2.6.2後上採樣框架 18
2.6.3交替上下採樣框架 18
2.6.4漸進式上採樣框架 19
2.7本章小結 20
第3章基於Transformer的遙感圖像超分辨率網絡 21
3.1研究動機 21
3.2總體網絡結構 22
3.3稀疏激活的自注意力編碼器 24
3.4 亞像素空間的多級融合解碼器 27
3.5 本章小結 28
第4章 實驗結果與分析 29
4.1 實驗環境及參數設置 29
4.2 實驗數據集 29
4.3 遙感圖像的重建質量評價方法 30
4.3.1 峰值信噪比 30
4.3.2 結構相似性 31
4.4 UCMerced 數據集上的超分辨率實驗 31
4.5 AID數據集上的超分辨率實驗 34
4.6 對網絡模型的討論與分析 37
4.7 本章小結 39
第5章 總結與展望 40
5.1 工作總結 40
5.2 研究展望 40
参考文献 42
作者簡歷 48
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