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 ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512 × 512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) 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 DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.

As manual labeling is very difficult and time-consuming, the labeled samples used to train a supervised classifier are generally limited, which become one of the biggest challenge for hyperspectral imagery classification. In order to tackle this issue, a recent trend is to exploit the structure information of materials, as which reflects the region homogeneity in the spatial domain and offers an invaluable complement to the spectral information. In this respect, 3-D Gabor wavelets have been introduced to extract joint spectral-spatial features for hyperspectral images. One the one hand, the features extracted by 3-D Gabor wavelets lead to very good performance for classification. On the other hand, its drawbacks, i.e., big number of features and high computational cost, limit its applicability. In this paper, a 3-D Gabor-wavelet-based phase coding and Hamming distance-based matching (3DGPC-HDM) framework is developed for hyperspectral imagery classification. The proposed method, instead of taking into account the large volume of Gabor magnitude features, exploits the Gabor phase features with certain orientations (i.e., the direction parallel to the spectral axis), which are then encoded by a simple quadrant bit coding scheme. After that, a normalized Hamming distance matching (HDM) method is adopted to determine the similarity of two samples, and the nearest neighbor classifier is routinely utilized for pixelwise recognition. Finally, experiments on three real hyperspectral data sets show that the proposed 3DGPC-HDM leads to very good performance. Comparisons with the state-of-the-art methods in the literature, in terms of both classifier complexity and generalization ability from very small training sets, are also included.

In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution.

Since it is usually difficult and time-consuming to obtain sufficient training samples by manually labeling, feature extraction, which investigates the characteristics of hyperspectral images (HSIs), such as spectral continuity and spatial locality of surface objects, to achieve the most discriminative feature representation, is very important for HSI classification. Meanwhile, due to the spatial regularity of surface materials, it is desirable to improve the classification performance of HSIs from the superpixel viewpoint. In this paper, we propose a novel local binary pattern (LBP)-based superpixel-level decision fusion method for HSI classification. The proposed framework employs uniform LBP (ULBP) to extract local image features, and then, a support vector machine is utilized to formulate the probability description of each pixel belonging to every class. The composite image of the first three components extracted by a principal component analysis from the HSI data is oversegmented into many homogeneous regions by using the entropy rate segmentation method. Then, a region merging process is applied to make the superpixels obtained more homogeneous and agree with the spatial structure of materials more precisely. Finally, a probability-oriented classification strategy is applied to classify each pixel based on superpixel-level guidance. The proposed framework “ULBP-based superpixel-level decision fusion framework” is named ULBP-SPG. Experimental results on two real HSI data sets have demonstrated that the proposed ULBP-SPG framework is more effective and powerful than several state-of-the-art methods.

With the development of hyperspectral imaging technology, hyperspectral images (HSIs) have become important when analyzing the class of ground objects. In recent years, benefiting from the massive labeled data, deep learning has achieved a series of breakthroughs in many fields of research. However, labeling HSIs requires sufficient domain knowledge and is time-consuming and laborious. Thus, how to apply deep learning effectively to small labeled samples is an important topic of research in HSI classification. To solve this problem, we propose a semisupervised Siamese network that embeds Siamese network into a semisupervised learning scheme. It integrates an autoencoder module and a Siamese network to, respectively, investigate information in a large amount of unlabeled data and rectify it with a limited labeled sample set, which is called 3DAES. First, the autoencoder method is trained on the massive unlabeled data to learn the refinement representation, creating an unsupervised feature. Second, based on this unsupervised feature, limited labeled samples are used to train a Siamese network to rectify the unsupervised feature to improve feature separability among various classes. Furthermore, by training the Siamese network, a random sampling scheme is used to accelerate training and avoid imbalance among various sample classes. Experiments on three benchmark HSI datasets consistently demonstrate the effectiveness and robustness of the proposed 3DAES approach with limited labeled samples. For study replication, the code developed for this study is available at https://github.com/ShuGuoJ/3DAES.git .