Abstract
Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the difference between cloudy and cloud-free images (i.e. clouds). Last, a generator (G) is used to fill in the different regions in cloud image in order to confuse the discriminator. Auto-GAN only requires images and their labels (1 for a cloud-free image, 0 for a cloudy image) in the training phase which is more time-saving to acquire than existing methods based on CNNs that require pixel-level labels. Auto-GAN is applied to cloud detection in Sentinel-2A Level 1C imagery. The results indicate that Auto-GAN method performs well in cloud detection over different land surfaces.
Highlights
Remote sensing images have been applied into many fields such as change detection, land cover and land use classification and environmental monitoring (Novo-Fernández et al, 2018)
A consists of seventy-three layers of operations, including convolution, transpose convolution, instance normalization, Rectified Linear Unit (ReLU), Leaky Rectified Linear Unit (LReLU) and sigmoid activation functions
To demonstrate the effectiveness of Auto-Generative adversarial networks (GANs), Sentinel-2A imagery were selected as training and testing data
Summary
Remote sensing images have been applied into many fields such as change detection, land cover and land use classification and environmental monitoring (Novo-Fernández et al, 2018). It is very difficult to set a threshold that can be used for other satellite images; 2) Method based on multi-threshold for multi-spectral bands is limited by the bands of satellite sensor (Parmes et al, 2017); 3) Multi-temporal methods require time series images which are not always available or practical to process They may be suitable for the specific regions, but do not work well in other regions. Multi-scale convolutional features are used to detect cloud in medium and high-resolution remote sensing images of different sensor (Li et al 2019) These DL-based methods have achieved very high accuracy for cloud detection in remote sensing images, they require pixel-level ground truths labeled by humans which are very time-consuming to obtain. Experimental results on Sentinel-2A images in China show that the proposed method performs well under different
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