Deep hashing algorithms are widely exploited in retrieval tasks due to its low storage and retrieval efficiency. Most of which focus on global feature learning, whilst neglecting local fine-grained features and saliency information for drone images. In this paper, we tackle these dilemmas with a novelDeep Saliency Smoothing Hashing(DSSH) algorithm, which can leverage saliency capture mechanism, distribution smoothing term, global features and local fine-grained features to learn effective hash codes for drone image retrieval. The DSSH algorithm first designs information extraction module to capture global features and local fine-grained features for drone images. Meanwhile, a saliency capture module is proposed to perform information interaction attention and visual enhancement attention, which can capture the saliency area of drone images effectively. On top of the two paths, a novel objective function is designed to preserve the similarity of hash codes, smooth the distribution of drone image datasets and reduce the quantization errors between hash codes and hash-like codes concurrently. Extensive experiments on the Drone Action Dataset and ERA Drone Dataset demonstrate that the DSSH algorithm can further improve the retrieval performance compared to other deep hashing algorithms.

Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current research mainly focuses on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies that are important for describing complicated dynamics. In this article, we propose a novel deep neural network, termed Fusing Temporal relations and Holistic features for aerial video classification (FuTH-Net), to model not only holistic features but also temporal relations for aerial video classification. Furthermore, the holistic features are refined by the multiscale temporal relations in a novel fusion module for yielding more discriminative video representations. More specially, FuTH-Net employs a two-pathway architecture: 1) a holistic representation pathway to learn a general feature of both frame appearances and short-term temporal variations and 2) a temporal relation pathway to capture multiscale temporal relations across arbitrary frames, providing long-term temporal dependencies. Afterward, a novel fusion module is proposed to spatiotemporally integrate the two features learned from the two pathways. Our model is evaluated on two aerial video classification datasets, ERA and Drone-Action, and achieves the state-of-the-art results. This demonstrates its effectiveness and good generalization capacity across different recognition tasks (event classification and human action recognition). To facilitate further research, we release the code athttps://gitlab.lrz.de/ai4eo/reasoning/futh-net.

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this paper, we create a new dataset, named Drone-Anomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from 7 different realistic scenes with various anomalous events. There are 87,488 color video frames (51,635 for training and 35,853 for testing) with the size of 640 × 640 at 30 frames per second. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, ANomaly Detection with Transformers (ANDT), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY. We make our dataset and code publicly available1.

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task-multi-scene recognition in single images. To this end, we create a large-scale dataset, called Mul-tiScene dataset, composed of 100,000 unconstrained images each with multiple labels from 36 different scenes. Among these images, 14,000 of them are manually interpreted and assigned ground-truth labels, while the remaining images are provided with crowdsourced labels, which are generated from low-cost but noisy OpenStreetMap (OSM) data. By doing so, our dataset allows two branches of studies: 1) developing novel CNNs for multi-scene recognition and 2) learning with noisy labels. We experiment with extensive baseline models on our dataset to offer a benchmark for multi-scene recognition in single images. Aiming to expedite further researches, we will make our dataset and pre-trained models available11https://github.com/Hua-YS/Multi-Scene-Recognition.

With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current researches mainly focus on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies which are important for describing complicated dynamics. In this paper, we propose a novel two-pathway network to model not only holistic features, but also temporal relations for aerial video classification. More specially, our model employs a two-pathway architecture: (1) a holistic representation pathway to learn a general feature of frame appearances and short-term temporal variations and (2) a temporal relation pathway to capture multi-scale temporal relations across arbitrary frames, providing long-term temporal dependencies. Our model is evaluated on event recognition dataset, ERA, and achieves the state-of-the-art results. This demonstrates its effectiveness and good generalization capacity.

By the virtue of high flexibility, low-cost, real-time, and high-resolution data acquisition capacity, unmanned aerial vehicles (UAVs) can be exploited for a wide range of applications, especially in surveillance, inspection, and search fields. Such applications aim to detect potential suspicious events, violent human actions from an untrimmed and lengthy UAV video. Anomaly detection methods are highly in demand because it is unrealistic for human experts to manually detect all abnormal events in image scene. However, anomaly detection methods in aerial videos are rarely studied in the remote sensing community. In this paper, We propose a future frame prediction network based on convolutional variational autoencoder networks to detect anomalous events. Compared to several models, our network has a superior performance.

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.

Unmanned aerial vehicles (UAVs) are now widespread available. Yet the more UAVs there are in the skies, the more video data they create. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on UAV video content understanding is of great importance. In this paper, we introduce a novel task of event recognition in unconstrained aerial videos in the remote sensing community and present a dataset for this task. Organized in a rich semantic taxonomy, the proposed dataset covers a wide range of events involving diverse environments and scales. We report results of plenty of deep networks in two ways: single-frame classification and video classification. The dataset and trained models can be downloaded from https://1cmou.github.io/ERA_Dataset/.

Aerial image scene classification is a fundamental problem for understanding high-resolution remote sensing images and has become an active research task in the field of remote sensing due to its important role in a wide range of applications. However, the limitations of existing datasets for scene classification, such as the small scale and low-diversity, severely hamper the potential usage of the new generation deep convolutional neural networks (CNNs). Although huge efforts have been made in building large-scale datasets very recently, e.g., the Aerial Image Dataset (AID) which contains 10,000 image samples, they are still far from sufficient to fully train a high-capacity deep CNN model. To this end, we present a larger-scale dataset in this paper, named as AID++, for aerial scene classification based on the AID dataset. The proposed AID++ consists of more than 400,000 image samples that are semi-automatically annotated by using the existing the geographical data. We evaluate several prevalent CNN models on the proposed dataset, and the results show that our dataset can be used as a promising benchmark for scene classification.

Discovering application configurations and dependencies in the existing runtime environment is a critical prerequisite to the success of cloud migration, which attracts many attentions from both researchers and commercial vendors. However, the high complexity and diversity of enterprise applications as well as their runtime environment challenge the existing approaches which generally depend on the pre-built domain specific knowledge. In this paper, we propose a generic framework for application configuration discovery which can be applied even when the domain knowledge is missing or incomplete. We design a generic approach to significantly narrow down the configuration discovery scale based on the iterative comparison and enable users to manually identify configurations from reasonable scaled file sets with semantic tags. To maximize automation, we further design an easy extensible and pluggable knowledge base to assist configuration discovery. Through extensive case study, the capability and efficiency of our framework have been demonstrated.