【摘要】：隨著遙感技術(shù)的不斷發(fā)展，如何自動、準(zhǔn)確、快速的為遙感圖像分類一直是研究熱點(diǎn)。由于紅樹林遙感圖像訓(xùn)練樣本獲取困難，訓(xùn)練樣本少，給自動化分類精度帶來了很大的考驗。本文在研究隨機(jī)森林算法的基礎(chǔ)上，結(jié)合TM圖像的特征，提出了隨機(jī)森林的改進(jìn)算法，提升了自動化水平。 隨機(jī)森林以其適用于小樣本、穩(wěn)定性強(qiáng)等特點(diǎn)被廣泛應(yīng)用于遙感分類，為了提升遙感分類的精度和效率，本文在隨機(jī)森林作基礎(chǔ)上作如下工作： 首先，為了提升TM遙感圖像的分類精度，提出了完全隨機(jī)的特征選取與組合的隨機(jī)森林，能自動提取、挖掘TM圖像中組合特征的信息。該算法是在特征線性組合的基礎(chǔ)上加入了對特征組合個數(shù)的隨機(jī)性和子特征空間大小的隨機(jī)性，降低了隨機(jī)森林的泛化誤差，提升了分類精度。 其次，為了提升隨機(jī)森林的分類效率，提出了基于克隆選擇的隨機(jī)森林，該算法引用人工免疫思想對隨機(jī)森林進(jìn)行選擇優(yōu)化，優(yōu)化后，很好的壓縮了隨機(jī)森林，分類效率更高，分類精度也進(jìn)一步提升。 最后，結(jié)合隨機(jī)森林的性質(zhì)，提出一個基于邊緣最大化的未標(biāo)簽樣本選取機(jī)制，實(shí)驗證明，以該機(jī)制所選樣本對提升隨機(jī)森林的泛化能力有積極貢獻(xiàn)。 為了驗證算法的有效性，，所提算法都在UCI數(shù)據(jù)集上驗證有效性，以保證通用性，并在遙感圖像上和傳統(tǒng)算法作進(jìn)一步對比分析。
文內(nèi)圖片：
圖片說明：TM圖像圖
[Abstract]:With the continuous development of remote sensing technology, how to classify remote sensing images automatically, accurately and quickly has been a hot research topic. Because of the difficulty of obtaining training samples of mangrove remote sensing images, there are few training samples, which brings a great test to the accuracy of automatic classification. Based on the research of random forest algorithm and the characteristics of TM image, an improved algorithm of random forest is proposed in this paper, which improves the automation level. Random forest is widely used in remote sensing classification because it is suitable for small samples and has strong stability. In order to improve the accuracy and efficiency of remote sensing classification, this paper does the following work on the basis of random forest. Firstly, in order to improve the classification accuracy of TM remote sensing image, a completely random feature selection and combination random forest is proposed, which can automatically extract and mine the information of combined features in TM images. On the basis of feature linear combination, the algorithm adds the randomness of the number of feature combinations and the randomness of the size of subspace, reduces the generalization error of random forest and improves the classification accuracy. Secondly, in order to improve the classification efficiency of random forest, a random forest based on clonal selection is proposed. The algorithm uses artificial immune idea to optimize the selection of random forest. After optimization, the random forest is compressed very well, the classification efficiency is higher, and the classification accuracy is further improved. Finally, combined with the properties of random forest, an untagged sample selection mechanism based on edge maximization is proposed. The experimental results show that the selected samples by this mechanism have a positive contribution to improving the generalization ability of random forest. In order to verify the effectiveness of the algorithm, the proposed algorithms are verified on the UCI dataset to ensure universality, and the remote sensing image is further compared with the traditional algorithm.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：2512337