Cloud detection is a challenging task but plays a major role for remote sensing image processing. Due to the diversity of cloud and the complexity of underlying surfaces, most of the current cloud detection methods still face great challenges, especially in detecting the thin cloud. Therefore, we propose a method to detect cloud pixels in GaoFen-1 WFV images. In our method, the deep encoder-decoder network is used to learn the multi-scale global features. So that the high-level semantic information obtained in the process of feature learning is integrated with low-level spatial information to classify images into cloud and non-cloud regions. In addition, Up and Down blocks using Harr wavelet transform are designed to fully exploit the structural information of images, and especially the texture information of the cloud can be learned targetedly. The experimental results indicate that the network using Up and Down blocks performs well under different scenes.