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黃土丘陵溝壑區(qū)遙感影像信息面向?qū)ο蠓诸惙椒ㄌ崛?/div>
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“十一五”國(guó)家科技支撐計(jì)劃資助項(xiàng)目(2006BAD09B03—03);中科院科學(xué)院西部行動(dòng)計(jì)劃資助項(xiàng)目(KZCX2—XB2—05)


Object-oriented Classification Approach for Remote Sensing Imagery Information Extraction in Loess Hilly-gully Region
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    摘要:

    探索了基于面向?qū)ο蠓诸惙椒ㄌ崛↑S土丘陵溝壑區(qū)高分辨率遙感影像土地信息的途徑,。以燕溝典型小流域?yàn)槔?,基于ALOS的多光譜,、全色立體影像并輔以數(shù)字高程模型DEM和NDVI數(shù)據(jù),,進(jìn)行面向?qū)ο蟮亩喑叨确指?,利用閾值逐次提取與該區(qū)生態(tài)系統(tǒng)恢復(fù)、農(nóng)業(yè)生產(chǎn)和生活實(shí)際密切相關(guān)的灌叢,、林地,、草地、耕地,、果園,、居住地和水體共7種土地利用類型,得到的分類精度為77.73%,。

    Abstract:

    The object-oriented classification approach was employed in loess hilly-gully region. Taking typical watershed Yan’gou as example, ALOS satellite images were used. The object-oriented multi-segmentation was carried out with multi-spectral, panchromatic imagery, auxiliary data of digital evaluation model (DEM) and the normalized difference vegetation index (NDVI). The land-use categories closely related to ecosystem restoration, farming and living, such as forest, grass, farmland, orchard, settlement and water, were classified with thresholds. The classification results had promising accuracies, and the overall classification accuracy was 77.73%.

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買(mǎi)凱樂(lè),張文輝.黃土丘陵溝壑區(qū)遙感影像信息面向?qū)ο蠓诸惙椒ㄌ崛J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(4):153-158. Mai Kaile, Zhang Wenhui. Object-oriented Classification Approach for Remote Sensing Imagery Information Extraction in Loess Hilly-gully Region[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(4):153-158.

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