本文爲英國蘭開斯特大學蘭開斯特環境中心（作者：Ce Zhang）的博士論文，共267頁。
近年來，隨着傳感器技術的發展，人們每天都要採集大量非常精細空間分辨率（VFSR）的遙感圖像。這些VFSR圖像呈現了光譜和空間複雜的精細空間細節，對土地覆蓋自動分類和土地利用分類提出了巨大挑戰。深度學習重新點燃了人工智能對通用機器的追求，使之能夠以自動化的方式執行任何與人類相關的任務。這在很大程度上是由深度機器學習中的興起浪潮驅動的，該類方法通過分層特徵表示對高層抽象進行建模，而無需人工設計特徵或規則，顯示了從VFSR圖像識別和表徵LC和LU模式的巨大潛力。
本文以深度卷積神經網絡（CNN）爲例，提出了一套新的用於LC和LU圖像分類的深度學習方法。然而，在使用VFSR圖像應用標準像素CNN進行LC和LU分類時遇到了一些困難，包括幾何畸變、邊界不確定性和巨大的計算冗餘。利用基於規則的決策融合或粗糙集理論的不確定性建模，解決了LC分類面臨的技術難題。針對土地利用問題，提出了一種基於目標的CNN方法，利用目標內部和目標之間的信息，對每個分割的目標（一組均勻像素）進行採樣和預測。因此，LU分類的準確性和效率都很高。LC和LU在同一地理空間中形成一個層次本體，並通過它們的聯合分佈來建模，其中LC和LU通過迭代同時進行分類。這些開發的深度學習技術達到了LC和LU的最高分類精度，高達大約90%的準確度，比現有的深度學習方法高5%，比傳統的基於像素和基於目標的方法高10%。該研究通過基於深度學習的創新在LC和LU分類中做出了重要貢獻，在廣泛的地理空間應用中具有巨大的潛在價值。
Recent advances in sensor technologies have witnessed a vastamount of very fine spatial resolution (VFSR) remotely sensed imagery beingcollected on a daily basis. These VFSR images present fine spatial details thatare spectrally and spatially complicated, thus posing huge challenges in automaticland cover (LC) and land use (LU) classification. Deep learning reignited thepursuit of artificial intelligence towards a general purpose machine to be ableto perform any human-related tasks in an automated fashion. This is largelydriven by the wave of excitement in deep machine learning to model thehigh-level abstractions through hierarchical feature representations withouthuman-designed features or rules, which demonstrates great potential inidentifying and characterising LC and LU patterns from VFSR imagery. In thisthesis, a set of novel deep learning methods are developed for LC and LU image classificationbased on the deep convolutional neural networks (CNN) as an example. Severaldifficulties, however, are encountered when trying to apply the standardpixelwise CNN for LC and LU classification using VFSR images, includinggeometric distortions, boundary uncertainties and huge computationalredundancy. These technical challenges for LC classification were solved eitherusing rule-based decision fusion or through uncertainty modelling using roughset theory. For land use, an object based CNN method was proposed, in whicheach segmented object (a group of homogeneous pixels) was sampled and predictedby CNN with both within-object and between-object information. LU was, thus,classified with high accuracy and efficiency. Both LC and LU formulate ahierarchical ontology at the same geographical space, and such representationsare modelled by their joint distribution, in which LC and LU are classifiedsimultaneously through iteration. These developed deep learning techniques achievedby far the highest classification accuracy for both LC and LU, up to around 90%accuracy, about 5% higher than the existing deep learning methods, and 10% greaterthan traditional pixel-based and object-based approaches. This research made a significantcontribution in LC and LU classification through deep learning based innovations,and has great potential utility in a wide range of geospatial applications.
- 引言
- 文獻回顧
- 用於高分辨率遙感圖像分類的混合MLP-CNN分類器
- 基於VPRS的高分辨率遙感圖像CNN和MRF分類區域決策融合
- 基於目標卷積神經網絡的城市土地利用分類
- 土地覆蓋與土地利用分類的聯合深度學習
- 討論與結論
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