Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.
This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultra high resolution traffic videos taken from an Unmanned Aerial Vehicle (UAV). We first capture nearly an hour-long ultra high resolution traffic video at 5 busy road intersections of a modern megacity by flying an UAV during the rush hours. We then randomly sampled over 17K 512x512 pixel image patches from the video frames and manually annotated over 64K vehicles to form a dataset for this research which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of advanced deep neural network based vehicle detection and localization, type (car, bus and truck) recognition, tracking and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced Single Shot Multibox Detector (Enhanced-SSD) outperforms other deep neural network based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultra high resolution video provides more information which enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultra high resolution video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic density estimation.
Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR performance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of super-resolved images (SRIs). In constructing the Quality Assessment Database for SRIs (QADS), we carefully selected 20 reference images and created 980 SRIs using 21 image SR methods. Mean opinion score (MOS) for these SRIs are collected through 100 individuals participating a suitably designed psychovisual experiment. Extensive numerical and statistical analysis is performed to show that the MOS of QADS has excellent suitability and reliability. The psychovisual experiment has led to the discovery that, unlike distortions encountered in other IQA databases, artifacts of the SRIs degenerate the image structure as well as image texture. Moreover, the structural and textural degenerations have distinctive perceptual properties. Based on these insights, we propose a novel method to assess the visual quality of SRIs by separately considering the structural and textural components of images. Observing that textural degenerations are mainly attributed to dissimilar texture or checkerboard artifacts, we propose to measure the changes of textural distributions. We also observe that structural degenerations appear as blurring and jaggies artifacts in SRIs and develop separate similarity measures for different types of structural degenerations. A new pooling mechanism is then used to fuse the different similarities together to give the final quality score for an SRI. Experiments conducted on the QADS demonstrate that our method significantly outperforms classical as well as current state-of-the-art IQA methods.
With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art.Index Terms-Deep learning, metric learning, content-based image retrieval (CBIR), remote sensing image retrieval (RSIR).
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