Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence, limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the L 1/2 sparsity constraint, which we name L 1/2 -NMF. The L 1/2 regularizer not only induces sparsity, but is also a better choice among Lq(0 < q < 1) regularizers. We propose an iterative estimation algorithm for L 1/2 -NMF, which provides sparser and more accurate results than those delivered using the L1 norm. We illustrate the utility of our method on synthetic and real hyperspectral data and compare our results to those yielded by other state-of-the-art methods.
With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-theart approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.
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.
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