Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images

With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of remotely sensed images. However, the exploration of these images still involves a tremendous amount of human interventions, which are tedious, time-consuming, and inefficient. To help imaging experts gain a complete understanding of the images and locate the objects of interest in a more accurate and efficient way, there is always an urgent need for developing automatic detection algorithms. In this work, we delve into the object detection problems in remote sensing applications, exploring the detection algorithms for both hyperspectral images (HSIs) and high resolution aerial images. In the first part, we focus on the subpixel target detection problem in HSIs with low spatial resolutions, where the objects of interest are much smaller than the image pixel spatial resolution. To this end, we explore the detection frameworks that integrate image segmentation techniques in designing the matched filters (MFs). In particular, we propose a novel image segmentation algorithm to identify the spatial-spectral coherent image regions, from which the background statistics were estimated for deriving the MFs. Extensive experimental studies were carried out to demonstrate the advantages of the proposed subpixel target detection framework. Our studies show the superiority of the approach when comparing to state-of-the-art methods. The second part of the thesis explores the object based image analysis (OBIA) framework for geospatial object detection in high resolution aerial images. Specifically, we generate a tree representation of the aerial images from the output of hierarchical image segmentation algorithms and reformulate the object detection problem into a tree matching task. We then proposed two tree-matching algorithms for the object detection framework. We demonstrate the efficiency and effectiveness of the proposed tree-matching based object detection framework. In the third part, we study object detection in high resolution aerial images from a machine learning perspective. We investigate both traditional machine learning based framework and end-to-end convolutional neural network (CNN) based approach for various object detection tasks. In the traditional detection framework, we propose to apply the Gaussian process classifier (GPC) to train an object detector and demonstrate the advantages of the probabilistic classification algorithm. In the CNN based approach, we proposed a novel scale transfer module that generates enhanced feature maps for object detection. Our results show the efficiency and competitiveness of the proposed algorithms when compared to state-of-the-art counterparts

Transfer Learning for High Resolution Aerial Image Classification

With rapid developments in satellite and sensor technologies, increasing amount of high spatial resolution aerial images have become available. Classification of these images are important for many remote sensing image understanding tasks, such as image retrieval and object detection. Meanwhile, image classification in the computer vision field is revolutionized with recent popularity of the convolutional neural networks (CNN), based on which the state-of-the-art classification results are achieved. Therefore, the idea of applying the CNN for high resolution aerial image classification is straightforward. However, it is not trivial mainly because the amount of labeled images in remote sensing for training a deep neural network is limited. As a result, transfer learning techniques were adopted for this problem, where the CNN used for the classification problem is pre-trained on a larger dataset beforehand. In this paper, we propose a specific fine-tuning strategy that results in better CNN models for aerial image classification. Extensive experiments were carried out using the proposed approach with different CNN architectures. Our proposed method shows competitive results compared to the existing approaches, indicating the superiority of the proposed fine-tuning algorith

Stepwise exhumation of the Triassic Lanling high-pressure metamorphic belt in Central Qiangtang, Tibet: Insights from a coupled study of metamorphism, deformation, and geochronology

The E-W trending Central Qiangtang metamorphic belt (CQMB) is correlated to the Triassic orogeny of the Paleo-Tethys Ocean prior to Cenozoic growth of the Tibetan Plateau. The well-exposed Lanling high-pressure, low-temperature (HP-LT) metamorphic complex was chosen to decipher the process by which it was exhumed, which thereby provides insights into the origin of the CQMB and Qiangtang terrane. After a detailed petrological and structural mapping, three distinct N-S-trending metamorphic domains were distinguished. Microscopic observations show that core domain garnet (Grt)-bearing blueschist was exhumed in a heating plus depressurization trajectory after peak eclogitic conditions, which is more evident in syntectonic vein form porphyroblastic garnets with zoning typical of a prograde path. Grt-free blueschist of the mantle domain probably underwent an exhumation path of temperature increasing and dehydration, as evidenced by pervasive epidote veins. The compilation of radiometric results of high-pressure mineral separates in Lanling and Central Qiantang, and reassessments on the published phengite data sets of Lanling using Arrhenius plots allow a two-step exhumation model to be formulated. It is suggested that core domain eclogitic rocks were brought onto mantle domain blueschist facies level starting at 244-230 Ma, with exhumation continuing to 227-223.4 Ma, and subsequently were exhumed together starting at 223-220 Ma, reaching lower greenschist facies conditions generally after 222-217 Ma. These new observations indicate that the CQMB formed as a Triassic autochthonous accretionary complex resulting from the northward subdcution of the Paleo-Tethys Ocean and that HP-LT rocks therein were very probably exhumed in an extensional regime.This work was jointly funded by the Young Scientist Fund of the National Natural Science Foundation of China (grant 41402177) and the Fundamental Research Funds for the Central Universities (2652014004). Our field work was supported by a project issued by the China Geological Survey (CGS): The integrated geological survey on the west part and central uplift of Qiangtang Block (grant 12120115026901)

Dauricine Attenuates Vascular Endothelial Inflammation Through Inhibiting NF-κB Pathway

Endothelial cells are the fundamental components of blood vessels that regulate several physiological processes including immune responses, angiogenesis, and vascular tone. Endothelial dysfunction contributes to the development of various diseases such as acute lung injury, and endothelial inflammation is a vital part of endothelial dysfunction. Dauricine is an extract isolated from Menispermum dauricum DC, a traditional Chinese medical plant that can be used for pharyngitis. In this work, we found that IL-1β-induced overexpression of intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin was inhibited by dauricine in primary human umbilical vein endothelial cells (HUVECs). Correspondingly, adhesion of human acute monocytic leukemia cell line (THP-1) to HUVECs was decreased by dauricine. Further studies showed that dauricine inhibited the activation of nuclear factor-κB (NF-κB) pathway in HUVECs stimulated with IL-1β. In vivo, dauricine protected mice from lipopolysaccharide (LPS)-induced acute lung injury. In lung tissues, the activation of NF-κB pathway and the expression of its downstream genes (ICAM-1, VCAM-1, and E-selectin) were decreased by dauricine, consistent with what was found in vitro. In summary, we concluded that dauricine could alleviate endothelial inflammation by suppressing NF-κB pathway, which might serve as an effective candidate for diseases related with endothelial inflammation

RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN

The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the second-order information (SOI) matrix to find a more accurate descent direction and step size. However, the huge SOI matrices bring significant computational and memory overheads in the traditional architectures like GPU and CPU. On the other side, the ReRAM-based process-in-memory (PIM) technology is suitable for the second-order training because of the following three reasons: First, PIM's computation happens in memory, which reduces data movement overheads; Second, ReRAM crossbars can compute SOI's inversion in $O\left(1\right)$ time; Third, if architected properly, ReRAM crossbars can perform matrix inversion and vector-matrix multiplications which are important to the second-order training algorithms. Nevertheless, current ReRAM-based PIM techniques still face a key challenge for accelerating the second-order training. The existing ReRAM-based matrix inversion circuitry can only support 8-bit accuracy matrix inversion and the computational precision is not sufficient for the second-order training that needs at least 16-bit accurate matrix inversion. In this work, we propose a method to achieve high-precision matrix inversion based on a proven 8-bit matrix inversion (INV) circuitry and vector-matrix multiplication (VMM) circuitry. We design \archname{}, a ReRAM-based PIM accelerator architecture for the second-order training. Moreover, we propose a software mapping scheme for \archname{} to further optimize the performance by fusing VMM and INV crossbar. Experiment shows that \archname{} can achieve an average of 115.8$\times$/11.4$\times$ speedup and 41.9$\times$/12.8$\times$energy saving compared to a GPU counterpart and PipeLayer on large-scale DNNs.Comment: 13pages, 13 figure

Effect of Microstructure on the Compressive Mechanical Properties of Ti-20Zr-6.5Al-4V Alloy

The effect of microstructure on the mechanical properties of Ti-20Zr-6.5Al-4V alloy after solution and aging treatment was investigated by compression tests. The results showed that the microstructure was consisted of a duplex phase αʺ + β structure after solution treatment. With increasing solution treatment temperature, the size of β phase grain increased and the amount of αʺ phase decreased. The ultimate compressive strength and elongation decreased with increasing solution treatment temperature, while the yield strength increased. After aging-treatment at 700 ºC for 1.5 h, the microstructure consisted of a large amount of globular α phase, a little amount of fine acicular α phase and bulk β phase for the samples solution-treated at 850 ºC for 0.5 h. With increasing initial solution treatment temperature, the amount of globular α phase and bulk β phase decreased, however, the amount and size of acicular α phase increased after aging treatment. The ultimate compressive strength and elongation decreased with increasing initial solution treatment temperature, whereas the yield strength firstly increased, and then slightly decreased.</p