本文關鍵詞： 城鎮(zhèn)植物群 圖像分類 物候遙感 波譜—位置分析　出處：《遙感技術與應用》2017年05期 　論文類型：期刊論文

【摘要】：遙感圖像植物群分類已被證明是植物群分布自動制圖快速有效的方法。然而,場景噪聲和植物群之間光譜可分性差等形成的負面影響,使傳統的分類方法無法滿足必要的精度要求。為了解決這個問題,提出了一種稱為SLPA的遙感圖像植物群分類方法。它由波譜—位置聯合分析(S-L分析)和植物物候遙感分析(PA)兩部分組成。通過向特征空間添加密度描述符以及在特征空間疊加冬、夏季圖像特征數據,可以將這兩類分析嵌入分類過程。這種改進增加了可用描述符的數量,使分類特征空間足夠豐富,以適應復雜分類;同時又降低了分類不確定性,使分類精度獲得顯著改善。精度測試顯示,增加S-L分析和物候分析,將使植物群分類的全局精度分別平均提高15.0%和29.3%。另外,由于采用二值鄰域均值替代灰度鄰域密度,使得加入S-L分析幾乎不引起運算復雜性增大。Matlab測試結果顯示,SLPA在城鎮(zhèn)植物群遙感自動分類方面具有魯棒和普適性。
[Abstract]:The classification of flora in remote sensing images has been proved to be a rapid and effective method for automatic mapping of flora distribution. However, the negative effects of scene noise and poor spectral separability among flora have been found. In order to solve this problem, the traditional classification method can not meet the necessary precision requirements. In this paper, a classification method of plant flora in remote sensing images called SLPA is proposed, which is based on spectral and position analysis (S-L analysis) and phytophenological remote sensing analysis (PAA). By adding density descriptor to feature space and overlaying winter in feature space. The summer image feature data can be embedded into the classification process. This improvement increases the number of available descriptors and makes the classification feature space abundant enough to adapt to complex classification. At the same time, the uncertainty of classification is reduced, and the accuracy of classification is improved significantly. The precision test shows that S-L analysis and phenological analysis are added. The global accuracy of plant taxonomy will be improved by 15.0% and 29.3, respectively. In addition, the binary neighborhood mean is used to replace the gray neighborhood density. The results of Matlab test show that SLPA is robust and universal in remote sensing automatic classification of urban flora.
【作者單位】： 上海辰山植物園;上海植物園;上海城市植物資源開發(fā)應用工程技術研究中心上海植物園;華東師范大學地理科學學院;
【基金】：十二五國家科技支撐計劃項目“綠地低碳效益綜合提升和評價技術研究”(2013BAJ02B01-4)資助
【分類號】：S731;TP751
【正文快照】： 1　引　言植物群類別是生態(tài)功能定量估算的一個重要變量,比如它曾用于植物生物量和凈生產力的估算[1],也曾用于與植物生態(tài)功能相關的生物多樣性[2]、動物和昆蟲的棲息地質量[3]、植物碳儲存量[4]等的評估。因此,開發(fā)植物群類別識別技術對于生態(tài)建模非常重要。野外光譜測試表明，

本文編號：1465271