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Recent years have witnessed a dramatic evolution in small-scale remote sensors such as Unmanned aerial vehicles (UAVs). Characteristics such as automatic flight control, flight time, and image acquisition have fueled various computer-vision tasks, providing better efficiency and usefulness than fixed viewing surveillance cameras. However, in constrained scenarios, the number of UAV-based aerial datasets is still low, which comparatively focuses on specific tasks such as image segmentation. In this paper, we present a high-resolution UAV-based image-dataset, named "NITRDrone" focusing on aerial image segmentation tasks especially extracting the road networks from the aerial images. The images and video sequences in this dataset are captured over different locations of the NITR campus area, covering around 650 acres. Thus, it provides many diversified scenarios to be considered while analyzing aerial images. In particular, the dataset is prepared to address the existing challenges in UAV-based aerial image segmentation problems. Extensive experiments have been conducted to prove the effectiveness of the proposed dataset to address the aerial segmentation problems through the existing state-of-the-art methodologies. Out of the considered baseline methodologies, U-Net performs the best with an intersection of union (IoU) of 0.77, followed DeepLabplusException (IoU: 0.74) and SegNet (IoU: 0.68). We hope the NITRDrone dataset will encourage the researchers while boosting the research and development in the visual analysis of UAV platforms. The NITRDrone dataset is available online at: [https://github.com/drone-vision/NITRDrone-Dataset]. [ABSTRACT FROM AUTHOR]

In traditional information hiding methods, the secret information is embedded into the carriers, which will inevitably leave traces of modification on the carriers. In those methods, the modified images can be easily detected by some steganalysis algorithm and thus the security can not be guaranteed. To address this problem, the concept of coverless information hiding is proposed. However, general coverless information hiding method has a lower information hiding capacity. In this paper, we propose a novel coverless information hiding method based on the average pixel values of sub-images. We generate hash sequences by a hashing algorithm and realize the secret information hiding through mapping relationship. In the first place, we build a dictionary and a hash array. Then we map the dictionary and the hash array through mapping relationship. Furthermore, we build a multi-level index structure for retrieving the stego-images efficiently. The experimental results and analysis show that our method has a good performance in the capacity of information hiding, the security, the robustness to image attacks and the hiding success rate based on different image databases. [ABSTRACT FROM AUTHOR]