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          • 軟件名稱:基于面向對象與深度學習的榆樹疏林識別方法研究
          • 軟件大小: 0.00 B
          • 軟件評級: ★★★★★★
          • 開 發 商: 陳昂, 楊秀春, 徐斌, 金云翔, 張文博, 郭劍, 邢曉語, 楊東
          • 軟件來源: 《地球信息科學學報》
          • 解壓密碼:www.zgkqyl.com

          資源簡介

          摘要:

          榆樹疏林是渾善達克沙地中一種特殊的植被類型,它對于維持區域生態系統穩定具有重要意義,在防風固沙、涵養水源、調節氣候等方面發揮著重要的作用。本文利用無人機影像與GF-2影像,對高分辨率數據源中榆樹疏林的兩種自動識別方法進行了研究。在面向對象方法中,首先通過計算影像對象的局部方差變化率得到了最佳分割尺度;其次采用隨機森林算法對初選特征的重要性進行排序,并刪除無關特征;最后分別對支持向量機(SVM)、隨機森林(RF)、深度神經網絡(DNN)3種分類器進行參數尋優與榆樹疏林提取。此外,在ENVI5.5中基于TensorFlow框架,利用U-Net構建深度學習模型對榆樹疏林進行了提取,并與面向對象方法進行對比。結果顯示:① 通過面向對象方法過程的優化,最終的識別精度較以往研究有所提升,GF-2影像中SVM總體精度為90.14%,RF總體精度為 90.57%,DNN總體精度為91.14%;無人機影像中SVM總體精度為97.70%, RF與DNN總體精度為97.42%。② 深度學習方法中,GF-2影像的總體精度為91.00%,無人機影像的總體精度達到了98.43%。研究結果說明在榆樹疏林提取中,無人機影像具有更高的空間分辨率,更豐富的紋理、形狀等信息,能達到比GF-2影像更高的精度。面向對象方法對于2種影像都有較高的適用性;深度學習的方法在本文中更適用于無人機影像,它可以有效地減少無人機影像中的錯分現象。

          關鍵詞: 榆樹疏林, 無人機, 面向對象, 機器學習, 深度學習, 渾善達克沙地

          Abstract:

          Elm sparse forest is a special vegetation type in Hunsh and ake sandy land. It has important significance for maintaining the stability of regional ecosystem, and plays a key role in sand fixation, water conservation and climate regulation. Rapid and accurate access to the distribution of elm sparse forest is conducive to the protection of the fragile ecosystem in the area. In this paper, the automatic recognition methods of elm sparse forest in high spatial resolution data source was studied by using Unmanned Aerial Vehicle(UAV) image and GF-2 image. After processing the original images of UAV, the Digital Ortho photo Map and the Canopy Height Model were obtained. The preprocessing of GF-2 data included atmospheric correction, ortho-rectification, image fusion et al. In the object-based method, firstly, the optimal segmentation scale was obtained by calculating the change rate of local variance in the image objects; Secondly, the importance of the selected features was sorted by the random forest algorithm, and the irrelevant features were deleted; Finally, the parameters of three classifiers, namely, Support Vector Machine(SVM), Random Forest(RF) and Deep Neural Network(DNN), were optimized, and then they were used to identify the elm sparse forest. In addition, based on the Tensor Flow framework in ENVI 5.5, a deep learning model based on U-Net was constructed to identify elm sparse forest. The results showed that: (1) through the optimization of the object-based method process, the final recognition accuracy was improved than the privious study. In GF-2 image, the overall accuracy of SVM was 90.14%, the overall accuracy of RF was 90.57%, and the overall accuracy of DNN was 91.14%. In UAV image, the overall accuracy of SVM was 97.70%, and the overall accuracy of RF and DNN were 97.42%.(2) In the deep learning method, the overall accuracy of the GF-2 image was 91%, and the overall accuracy of the UAV image reached 98.43%. The results illustrated that UAV image can achieve higher accuracy than GF-2 image in elm sparse forest recognition because of its higher spatial resolution, richer texture and shape information. Object-based method had high applicability for both kinds of images, and the accuracy of three classifiers were similar.The deep learning method was more suitable for UAV image in this paper, it can effectively reduce the misclassification phenomenon in UAV image.In the future, a higher quantity and quality sample database should be constructed to further improve the accuracy of deep learning method and provide support for the management and research of elm sparse forest.

          Key words: Elm sparse forest, UAV, object-based method, machine learning, deep learning, Hunshandake sandy land

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