Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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For video summarization and retrieval, one of the important modules is to group temporal-spatial coherent shots into high-level semantic video clips namely scene segmentation. In this paper, we propose a novel scene segmentation and categorization approach using normalized graph cuts(NCuts). Starting from a set of shots, we first calculate shot similarity from shot key frames. Then by modeling scene segmentation as a graph partition problem where each node is a shot and the weight of edge represents the similarity between two shots, we employ NCuts to find the optimal scene segmentation and automatically decide the optimum scene number by Q function. To discover more useful information from scenes, we analyze the temporal layout patterns of shots, and automatically categorize scenes into two different types, i.e. parallel event scenes and serial event scenes. Extensive experiments are tested on movie, and TV series. The promising results demonstrate that the proposed NCuts based scene segmentation and categorization methods are effective in practice.

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