Large-Scale, Multiple Level-of-Detail Change Detection from Remote Sensing Imagery Using Deep Visual Feature Clustering

In the era of big data, where massive amounts of remotely sensed imagery can be obtained from various satellites accompanied by the rapid change in the surface of the Earth, new techniques for large-scale change detection are necessary to facilitate timely and effective human understanding of natural and human-made phenomena. In this research, we propose a chip-based change detection method that is enabled by using deep neural networks to extract visual features. These features are transformed into deep orthogonal visual features that are then clustered based on land cover characteristics. The resulting chip cluster memberships allow arbitrary level-of-detail change analysis that can also support irregular geospatial extent based agglomerations. The proposed methods naturally support cross-resolution temporal scenes without requiring normalization of the pixel resolution across scenes and without requiring pixel-level coregistration processes. This is achieved with configurable spatial locality comparisons between years, where the aperture of a unit of measure can be a single chip, a small neighborhood of chips, or a large irregular geospatial region. The performance of our proposed method has been validated using various quantitative and statistical metrics in addition to presenting the visual geo-maps and the percentage of the change. The results show that our proposed method efficiently detected the change from a large scale area.

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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Research on highly parallel embedded control system design and implementation method

Impact ◽

10.21820/23987073.2019.10.44 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 44-46

Author(s):

Masato Edahiro ◽

Masaki Gondo

Keyword(s):

Computer Architecture ◽

Intelligent Systems ◽

Large Scale ◽

General Purpose ◽

Heterogeneous Structure ◽

Single Chip ◽

Powertrain Control ◽

Processing Power ◽

Hardware Description ◽

Many Core

The pace of technology's advancements is ever-increasing and intelligent systems, such as those found in robots and vehicles, have become larger and more complex. These intelligent systems have a heterogeneous structure, comprising a mixture of modules such as artificial intelligence (AI) and powertrain control modules that facilitate large-scale numerical calculation and real-time periodic processing functions. Information technology expert Professor Masato Edahiro, from the Graduate School of Informatics at the Nagoya University in Japan, explains that concurrent advances in semiconductor research have led to the miniaturisation of semiconductors, allowing a greater number of processors to be mounted on a single chip, increasing potential processing power. 'In addition to general-purpose processors such as CPUs, a mixture of multiple types of accelerators such as GPGPU and FPGA has evolved, producing a more complex and heterogeneous computer architecture,' he says. Edahiro and his partners have been working on the eMBP, a model-based parallelizer (MBP) that offers a mapping system as an efficient way of automatically generating parallel code for multi- and many-core systems. This ensures that once the hardware description is written, eMBP can bridge the gap between software and hardware to ensure that not only is an efficient ecosystem achieved for hardware vendors, but the need for different software vendors to adapt code for their particular platforms is also eliminated.

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A Review on Terahertz Technologies Accelerated by Silicon Photonics

Nanomaterials ◽

10.3390/nano11071646 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1646

Author(s):

Jingya Xie ◽

Wangcheng Ye ◽

Linjie Zhou ◽

Xuguang Guo ◽

Xiaofei Zang ◽

...

Keyword(s):

Integrated Circuit ◽

Silicon Photonics ◽

Large Scale ◽

Cost Effective ◽

Single Chip ◽

Photonic Integrated Circuit ◽

Hybrid Silicon ◽

The Cost ◽

Silicon Based ◽

Thz Applications

In the last couple of decades, terahertz (THz) technologies, which lie in the frequency gap between the infrared and microwaves, have been greatly enhanced and investigated due to possible opportunities in a plethora of THz applications, such as imaging, security, and wireless communications. Photonics has led the way to the generation, modulation, and detection of THz waves such as the photomixing technique. In tandem with these investigations, researchers have been exploring ways to use silicon photonics technologies for THz applications to leverage the cost-effective large-scale fabrication and integration opportunities that it would enable. Although silicon photonics has enabled the implementation of a large number of optical components for practical use, for THz integrated systems, we still face several challenges associated with high-quality hybrid silicon lasers, conversion efficiency, device integration, and fabrication. This paper provides an overview of recent progress in THz technologies based on silicon photonics or hybrid silicon photonics, including THz generation, detection, phase modulation, intensity modulation, and passive components. As silicon-based electronic and photonic circuits are further approaching THz frequencies, one single chip with electronics, photonics, and THz functions seems inevitable, resulting in the ultimate dream of a THz electronic–photonic integrated circuit.

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Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219834611 ◽

2019 ◽

Vol 41 (13) ◽

pp. 3612-3625 ◽

Cited By ~ 1

Author(s):

Wang Qian ◽

Wang Qiangde ◽

Wei Chunling ◽

Zhang Zhengqiang

Keyword(s):

Neural Networks ◽

Tracking Control ◽

Large Scale ◽

Inverse Dynamics ◽

Small Neighborhood ◽

High Order ◽

Interconnected Systems ◽

Delay Systems ◽

Rbf Neural Networks ◽

Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

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Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2015.02.008 ◽

2015 ◽

Vol 107 ◽

pp. 64-76 ◽

Cited By ~ 24

Author(s):

Thu Trang Lê ◽

Abdourrahmane M. Atto ◽

Emmanuel Trouvé ◽

Akhmad Solikhin ◽

Virginie Pinel

Keyword(s):

Time Series ◽

Change Detection ◽

Change Analysis

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Learning spatio-temporal visual features by a large scale neural network model

Neuroscience Research ◽

10.1016/j.neures.2010.07.1925 ◽

2010 ◽

Vol 68 ◽

pp. e434

Author(s):

Naoto Yukinawa ◽

Shin Ishii

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Large Scale ◽

Visual Features ◽

Spatio Temporal

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Single Chip Microcomputer Cluster Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.933.584 ◽

2014 ◽

Vol 933 ◽

pp. 584-589

Author(s):

Zhi Chun Zhang ◽

Song Wei Li ◽

Wei Ren Wang ◽

Wei Zhang ◽

Li Jun Qi

Keyword(s):

Real Time ◽

Management System ◽

Large Scale ◽

Fault Tolerant ◽

Single Chip ◽

Single Chip Microcomputer ◽

Cluster Management ◽

The Real ◽

Time Performance ◽

Operational Phase

This paper presents a system in which the cluster devices are controlled by single-chip microcomputers, with emphasis on the cluster management techniques of single-chip microcomputers. Each device in a cluster is controlled by a single-chip microcomputer collecting sample data sent to and driving the device by driving data received from the same cluster management computer through COMs. The cluster management system running on the cluster management computer carries out such control as initial SCM identification, run time slice management, communication resource utilization, fault tolerance and error corrections on single-chip microcomputers. Initial SCM identification is achieved by signal responses between the single-chip microcomputers and the cluster management computer. By using the port priority and the parallelization of serial communications, the systems real-time performance is maximized. The real-time performance can be adjusted and improved by increasing or decreasing COMs and the ports linked to each COM, and the real-time performance can also be raised by configuring more cluster management computers. Fault-tolerant control occurs in the initialization phase and the operational phase. In the initialization phase, the cluster management system incorporates unidentified single-chip microcomputers into the system based on the history information recorded on external storage media. In the operational phase, if an operation error of reading and writing on a single-chip microcomputer reaches a predetermined threshold, the single-chip microcomputer is regarded as serious fault or not existing. The cluster management system maintains accuracy maintenance database on external storage medium to solve nonlinear control of specific devices and accuracy maintenance due to wear. The cluster management system uses object-oriented method to design a unified driving framework in order to enable the implementation of the cluster management system simplified, standardized and easy to transplant. The system has been applied in a large-scale simulation system of 230 single-chip microcomputers, which proves that the system is reliable, real-time and easy to maintain.

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Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-367-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 367-374 ◽

Cited By ~ 1

Author(s):

R. R. Colditz ◽

R. M. Llamas ◽

R. A. Ressl

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Spatial Resolution ◽

Spatial Scales ◽

Time Interval ◽

Data Sets ◽

Change Analysis ◽

Dimensional Error ◽

Analysis Technique ◽

Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

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