基于红边位置变化特征的油菜种植区域提取

油菜种植区域的准确提取是进行长势监测、估产和灾情评估的前提,遥感手段已成为作物种植区域提取与监测的高效方法,而高光谱遥感具有波段多、波谱分辨率高、信息丰富等特点,因而提供了一种新的技术手段。为了探究高光谱遥感在作物识别与提取中的优势,该文基于2004年4月4日和5月6日2期EO-1 Hyperion影像,依据油菜盛花期到荚果期红边位置"蓝移"特征,区别于其他植被,实现了油菜种植区域的高光谱遥感提取。采用随机点验证方法对提取结果进行验证,总体精度为92.6%,Kappa系数为0.803,漏分和错分误差均处在合理的范围之内。该方法充分利用了高光谱遥感手段可提取植被红边参数的优势,且算法对地物光谱差异性不敏感,为油菜种植区域遥感提取提供了一套全新的思路与解决方案。

Extracting oilseed rape growing regions based on variation characteristics of red edge position

Abstract: The accurate extraction of oilseed rape growing regions is the premise of growth monitoring, yield estimation and disaster assessment, and remote sensing has been proved to be an effective way for this task. Hyperspectral remote sensing has the features of many more bands, higher spectral resolution, being rich in information and so on, which provides new technical means for extracting oilseed rape growing regions. Changxing, the county with the largest oilseed rape cultivated area in Zhejiang Province was selected to be the study area in this paper, and two EO-1 Hyperion images acquired on April 4th and May 6th, 2004 were adopted, corresponding to the full-bloom stage and pod stage respectively for oilseed rape growing in this region. After detailedly preprocessing of L1R data, the "linear four-point interpolation" method was adopted to get the red edge position (REP) of both stages. REP statistical histograms of typical oilseed rape growing regions and woodland covering both stages were generated and analyzed. The result showed that for both oilseed rape and woodland, the histograms spanning the two stages didn't have overlaps, and for oilseed rape, the REP value demonstrated an obviously "blue shift" characteristic from April 4th to May 6th. On the contrary, the REP value of woodland demonstrated clearly "red shift" characteristic during the same period, which was distinct from oilseed rape. Ignoring other over wintering crops like winter wheat because of the small amount of planting in Changxing that year, and according to the unique "blue shift" characteristic for oilseed rape from full-bloom stage to pod stage, differing from other vegetations, a "decision tree" was built, containing the algorithms of eliminating non-vegetation areas, non-oilseed rape growing regions, and pseudo-growing regions. Then oilseed rape growing regions were extracted based on this method. Next, the result was verified through 500 random sampling points combined with a visual interpretation method, the points were generated within the common coverage of both images, and among them 124 points were located in oilseed rape growing regions. The verification was performed employing an ENVI-ROI tool, and the result showed that the omission error and commission error were 13.7% and 15.7% respectively, total accuracy was 92.6%, and the Kappa coefficient reached up to 0.803, all above indicating that the research had achieved good results. The method provided by this paper takes full advantage of hyperspectral remote sensing, which can retrieve red edge parameters of vegetation with only a few bands, and gets rid of traditional information extracting methods like spectral match. Moreover, the accuracy is not sensitive to spectral differences of surface features, so it has every qualification to be a set of new ideas and solutions for extraction of oilseed rape growing regions by remote sensing.