CIFNet: context information fusion network for cloud and cloud shadow detection in optical remote sensing imagery

Abstract

Accurately and automatically detecting cloud and cloud shadow is one of the key steps in the analysis of optical remote sensing imagery. Currently, most cloud and cloud shadow detection methods are prone to false detection, and some clouds and cloud shadows may be missing from the detection results. Due to the lack of contextual information extraction and fusion capabilities, the accuracy of these cloud detection algorithms cannot be guaranteed. This article proposes a deep learning-based convolutional neural network context information fusion network (named CIFNet) to solve the problem of cloud and cloud shadow detection on Gaofen-1 wide field of view optical remote sensing imageries. First, we preprocess the dataset and make the corresponding labels. Next, we design a Resblock-cloud to capture global and local features and prevent the network from degrading. Then, we utilize a global context fusion module to fuse different levels of global context information by dense skip-connections. Finally, a multiscale context fusion module is designed to extract multiscale contextual relations between cloud and cloud shadow. Experimental results show that the proposed CIFNet method can obtain better cloud and cloud shadow boundaries in complex situations and therefore outperforms the competing methods.

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

“…In recent years, deep learning technology has demonstrated strong capabilities in many fields [20][21][22][23][24][25][26], the development of deep learning has significantly promoted the reform of the financial industry, and, at the same time, brought new ideas to the research of financial fraud detection. In 2016, Fu et al [27] proposed a CNN-based fraud detection network, which learns the intrinsic patterns of fraudulent behavior from labeled data to identify whether there is fraudulent behavior in each transaction sample.…”