Publications

Zhai, H; Zhang, HY; Zhang, LP; Li, PX (2018). Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 144, 235-253.

Abstract
Cloud and cloud shadow detection is a necessary preprocessing step for optical remote sensing applications because of the huge negative influence cloud and cloud shadow can have on data analysis. However, most of the existing cloud/shadow detection methods are designed based on specific band configurations of certain sensors, and their working mechanisms are relatively complex and computationally complicated, which limits their application. In view of this, in this paper, a unified cloud/shadow detection algorithm based on spectral indices (CSD-SI) is proposed for most of the widely used multi/hyperspectral optical remote sensing sensors with both visible and infrared spectral channels. On the one hand, the cloud index (CI) and cloud shadow index (CSI) are proposed to indicate the potential clouds and cloud shadows based on their physical reflective characteristics. In addition, considering the spatial coexistence of cloud and cloud shadow, a spatial matching strategy is utilized to remove the pseudo cloud shadows. The effectiveness of the proposed CSD-SI algorithm is demonstrated on eight different types of widely used multi/hyperspectral optical sensors, with different spectral and spatial resolution levels. Overall, in the experiments undertaken in this study, CSD-SI achieved a mean overall accuracy of 98.52% for cloud, with a mean producer's accuracy of 93.13% and a mean user's accuracy of 98.13%. For cloud shadow, CSD-SI achieved a means producer's accuracy of 84.33% and a mean user's accuracy of 89.12%. The experimental results show that the proposed CSD-SI method based on spectral indices can obtain a comparable cloud/shadow detection performance to that of the other state-of-the-art methods.

DOI:
10.1016/j.isprsjprs.2018.07.006

ISSN:
0924-2716