农业工程学报
農業工程學報
농업공정학보
2015年
3期
190-198
,共9页
陈杰%陈铁桥%刘慧敏%梅小明%邵权斌%邓敏
陳傑%陳鐵橋%劉慧敏%梅小明%邵權斌%鄧敏
진걸%진철교%류혜민%매소명%소권빈%산민
遥感%图像分割%支持向量机%角点%耕地%高分辨率遥感影像
遙感%圖像分割%支持嚮量機%角點%耕地%高分辨率遙感影像
요감%도상분할%지지향량궤%각점%경지%고분변솔요감영상
remote sensing%image segmentation%support vector machine (SVM)%corner%farmland%high-resolution remote sensing imagery
随着城市化建设进程的加快，城郊耕地经常会被开发为建设用地，甚至还会遭受非法占用的危险，这极大威胁了中国粮食安全。该文针对高分辨率遥感影像城郊耕地特点，提出了一种多尺度分层的耕地提取方法。首先，基于归一化植被指数（normalized difference vegetation index，NDVI）约束改进传统Harris角点检测方法得到建筑区概率密度图，并利用最大类间方差（Otsu algorithm，Otsu）分割去除复杂建筑区；然后，利用尺度选择工具（estimation of scale parameter， ESP）分析耕地占主导影像的多尺度分割结果，得到耕地较佳分割尺度并在该尺度下分割整幅影像；进而，利用形状、光谱信息初步检测出耕地对象，选择非建筑区的耕地与建筑区的非耕地样本，训练支持向量机模型并对不确定地物进行分类；最后，依据空间关系进一步判断图像对象，得到城郊耕地最终提取结果。试验结果表明，该方法能较高精度地从城郊区域的复杂背景中提取出不同类型、不同光谱的耕地目标。
隨著城市化建設進程的加快，城郊耕地經常會被開髮為建設用地，甚至還會遭受非法佔用的危險，這極大威脅瞭中國糧食安全。該文針對高分辨率遙感影像城郊耕地特點，提齣瞭一種多呎度分層的耕地提取方法。首先，基于歸一化植被指數（normalized difference vegetation index，NDVI）約束改進傳統Harris角點檢測方法得到建築區概率密度圖，併利用最大類間方差（Otsu algorithm，Otsu）分割去除複雜建築區；然後，利用呎度選擇工具（estimation of scale parameter， ESP）分析耕地佔主導影像的多呎度分割結果，得到耕地較佳分割呎度併在該呎度下分割整幅影像；進而，利用形狀、光譜信息初步檢測齣耕地對象，選擇非建築區的耕地與建築區的非耕地樣本，訓練支持嚮量機模型併對不確定地物進行分類；最後，依據空間關繫進一步判斷圖像對象，得到城郊耕地最終提取結果。試驗結果錶明，該方法能較高精度地從城郊區域的複雜揹景中提取齣不同類型、不同光譜的耕地目標。
수착성시화건설진정적가쾌，성교경지경상회피개발위건설용지，심지환회조수비법점용적위험，저겁대위협료중국양식안전。해문침대고분변솔요감영상성교경지특점，제출료일충다척도분층적경지제취방법。수선，기우귀일화식피지수（normalized difference vegetation index，NDVI）약속개진전통Harris각점검측방법득도건축구개솔밀도도，병이용최대류간방차（Otsu algorithm，Otsu）분할거제복잡건축구；연후，이용척도선택공구（estimation of scale parameter， ESP）분석경지점주도영상적다척도분할결과，득도경지교가분할척도병재해척도하분할정폭영상；진이，이용형상、광보신식초보검측출경지대상，선택비건축구적경지여건축구적비경지양본，훈련지지향량궤모형병대불학정지물진행분류；최후，의거공간관계진일보판단도상대상，득도성교경지최종제취결과。시험결과표명，해방법능교고정도지종성교구역적복잡배경중제취출불동류형、불동광보적경지목표。
Farmland is the material base of human survival and development. Currently, China faces the serious situation that a large population corresponds to less average arable land during a long term. As Chinese urbanization process has accelerated in recent years, farmland in particular area - suburb is often developed to construction land, and even suffers the risk of being illegally occupied. With the implementation of geography national condition monitoring plan on a national scale, China is in urgent need of the development of efficient extraction and monitoring method of farmland for the protection and rational utilization of farmland. High-resolution remote sensing image contains rich and detailed ground information, and it can accurately reflect the suburb terrain types and their spatial distribution. However, house, road, drainage, tree are mixed with farmland in the high-resolution remote sensing image of suburb, and the suburban grounds’ features are very similar in the spectrum, shape and texture characteristics, which leads the extraction of farmland to become very difficult. It is more feasible to extract the farmland from the non-construction area, therefore, the construction area is separated out from the image in the first place. The best segmentation scale suitable for farmland is determined according to the multi-scale segmentation in order to accomplish the extraction of farmland in an object-based approach, and then the whole image is segment in this best scale. Furthermore, the typical samples of farmland and non-farmland are selected to train the support vector machine (SVM) model. After the farmland has been classified via SVM, the spatial distribution relationship between segmented objects is taken into consideration to remove the false alarm objects and offset the omitted objects. Specifically, the proposed method consists of four steps: construction area removing, hierarchical farmland extraction, classification via SVM, and judgment by spatial distribution relationship. As a result, according to the characteristics of the suburban farmland in high-resolution remote sensing image, an automatic farmland extraction method combining multi-scale segmentation and hierarchical recognition is presented in this paper. Firstly, an improved algorithm of Harris corner detection constrained by NDVI is developed to extract the corner, and based on the probability density map of built-up areas, the complex construction areas are separated by using Otsu algorithm (OTSU). Secondly, the estimation of scale parameter (ESP) is used to analyze the parameters for generating the multi-scale segmentation of the non-construction area, and then the optimal ones suitable for extracting farmland are selected to segment the image as a whole. Thirdly, based on three rules for identifying farmland objects, shape and spectrum are intergraded to extract the typical farmland objects from a mass of segmented objects; after the SVM model is trained based on the intact farmland samples and the non-farmland samples in construction areas, this model is used to classify the remaining uncertain ground objects. Finally, the spatial distribution relationship is taken into account to refine the classification results produced by SVM. In this study, high-resolution remote sensing image from QuickBird is used to precisely extract farmland in suburb. In the farmland extraction experiments, correct rate of the proposed method is 80.09% which is 17.88% higher than the object-oriented SVM classification method, and error rate of the proposed method is 12.26% which is 1.30% lower than the object-oriented SVM classification method. The final results indicate that the proposed method can effectively extract farmland with different structures and spectral features from high-resolution remote sensing image of the complex environment of suburban area.
