MultiEvidence and MultiModal Fusion Network for GroundBased Cloud Recognition In recent times, deep neural networks have drawn much attention in groundbased cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for groundbased clouds. In this paper, we propose a novel method named multievidence and multimodal fusion network (MMFN) for groundbased cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from groundbased cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multimodal network in MMFN learns multimodal features for groundbased cloud. To fully fuse the multimodal and multievidence visual features, we design two fusion layers in MMFN to incorporate multimodal features with global and local visual features, respectively. Furthermore, we release the first multimodal groundbased cloud dataset named MGCD which not only contains the groundbased cloud images but also contains the multimodal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the stateoftheart methods, which validates its effectiveness for groundbased cloud recognition.

A Singlestage Multiclass Object Detection Method for Remote Sensing Images Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multiclass object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Singlestage Multiclass Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multiclass objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multiscale, multilevel feature map and get effective attention representation for multiclass remote sensing object detection. SMOD can be trained from scratch without pretrained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfromscratch method can obtain better performance compared with some stateofart algorithms on public multiclass remote sensing dataset AIIA20186.