基于多时相中巴资源卫星影像的冬小麦分类精度

中巴资源卫星2号星（CBERS-02）具有较高的空间分辨率和较丰富的光谱信息，对植被有较强的探测能力.利用2006—2007年北京地区冬小麦生育期早期的5景CBERS-02卫星影像，计算了各时相和不同时相组合的主要地物类型及冬小麦的光谱可分性距离，进行了监督分类，同时，结合高分辨率航空和卫星遥感影像，构建了训练样本和验证样本，对利用CBERS-02卫星提取的生育早期的冬小麦进行了时相分析和精度评价，并与同期TM影像提取结果进行对比.结果表明：时相是影响冬小麦分类的主要因素，不同光学传感器的遥感影像也会影响分类精度；多时相组合有利于提高冬小麦的提取精度，与单时相冬小麦提取的最高精度相比，最佳时相组合的制图精度提高了20.0%、用户精度提高了7.83%；与TM数据相比， CBERS-02卫星影像的冬小麦分类精度略低.

Accuracy of winter wheat identification based on multi-temporal CBERS-02 images.

Chinese-Brazi1 Earth Resources Satellite No.2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.