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  本文關鍵詞： 土地利用 決策樹 分類 土地覆被 光譜混合分解 多季相遙感數據 Landsat OLI　出處：《農業(yè)工程學報》2016年19期 　論文類型：期刊論文

【摘要】：該文利用2015年Landsat 8多季相數據,以民勤縣為例探討了干旱區(qū)土地覆被/利用分類方法。首先進行了研究區(qū)多季相遙感數據的主成分變換,確定了Landsat OLI光譜空間的內在維數。其次通過對各季相主成分空間的分析,確定了光譜混合分解的端元類型及各自的代表性季相。獲取端元光譜之后,采用全約束線性光譜混合模型分解各個季相的遙感影像,估計各端元組分占像元的面積百分比(端元豐度值)。最后利用端元豐度值的多季相估計結果,依據先驗知識和訓練樣本建立決策樹分類規(guī)則進行土地覆被/利用分類。分類方法的總體精度達到90.94%,Kappa系數為0.90。研究表明:將物理意義明確的端元豐度值作為分類變量,能夠快速、有效地獲取分類規(guī)則,生成樹的結構簡單、合理、清晰,對訓練數據的依賴性較低,因此具有應用于整個旱地系統(tǒng)土地覆被/利用分類的潛力。
[Abstract]:In this paper, the classification method of land cover / utilization in arid area is discussed by using Landsat 8 multi-season data in 2015, and Minqin County as an example. Firstly, the principal component transformation of multi-seasonal remote sensing data in the study area is carried out. The intrinsic dimension of the Landsat OLI spectral space is determined. Secondly, through the analysis of the principal component space of each seasonal phase, the endmember types of the spectral mixing decomposition and their representative seasonal phases are determined. A fully constrained linear spectral mixing model is used to decompose the remote sensing images of each seasonal phase, and the area percentage of each end component in the pixel is estimated (end component abundance value). Finally, the multi-quarterly estimation results of the end component abundance value are used. According to the prior knowledge and training samples, the classification rules of decision tree are established for land cover / use classification. The overall accuracy of the classification method is 90.94 and Kappa coefficient is 0.90. The results show that the abundance value of the end element with clear physical meaning is taken as the classification variable. It can obtain classification rules quickly and effectively, and the structure of the spanning tree is simple, reasonable and clear, and the dependence on training data is low, so it has the potential to be applied to land cover / use classification in the whole dryland system.
【作者單位】： 中國農業(yè)大學資源與環(huán)境學院;北京市農林科學院草業(yè)與環(huán)境研究發(fā)展中心;
【基金】：中國博士后科學基金資助項目(2016M591112)
【分類號】：F301.2

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