宋婉娟等:Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)
被阅读 985 次
2015-10-14
Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)
作者:Song, WJ (Song, Wanjuan)[ 1 ] ; Mu, XH (Mu, Xihan)[ 1 ] ; Yan, GJ (Yan, Guangjian)[ 1 ] ; Huang, S (Huang, Shuai)[ 1 ]
REMOTE SENSING
卷: 7  期: 8  页: 10425-10443
DOI: 10.3390/rs70810425
出版年: AUG 2015
 
摘要
Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d'Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products.
 
通讯作者地址: Mu, XH (通讯作者)
Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China