基于双层卷积神经网络的建筑物检测

IF 1 4区 心理学 Q3 PSYCHOLOGY, CLINICAL
P. Karuppusamy
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引用次数: 40

摘要

近年来,由于卷积神经网络在文本、音频和视频处理等领域的最先进性能,卷积神经网络的使用激增。然而,遥感应用领域还没有完全纳入CNN的使用。为了解决这个问题,我们介绍了一种新的CNN,它可以用来提高使用局部二进制模式(LBP)和定向梯度直方图(HOG)构建的检测器的性能。此外,在本文中,我们还使用了两个改进来提高CNN的准确性。第一个改进涉及使用欧拉方法进行特征向量变换,并结合归一化特征和原始特征。根据观察到的结果,我们还使用类似的方法进行了比较研究,结果表明,所提出的CNN比其他方法有所改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building Detection using Two-Layered Novel Convolutional Neural Networks
In the recent years, there has been a high surge in the use of convolutional neural networks (CNNs) because of the state-of-the art performance in a number of areas like text, audio and video processing. The field of remote sensing applications is however a field that has not fully incorporated the use of CNN. To address this issue, we introduced a novel CNN that can be used to increase the performance of detectors built that use Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Moreover, in this paper, we have also increased the accuracy of the CNN using two improvements. The first improvement involves feature vector transformation with Euler methodology and combining normalized and raw features. Based on the results observed, we have also performed a comparative study using similar methods and it has been identified that the proposed CNN proves to be an improvement over the others.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
20
期刊介绍: This journal is devoted to the application of theory and research from social psychology toward the better understanding of human adaptation and adjustment, including both the alleviation of psychological problems and distress (e.g., psychopathology) and the enhancement of psychological well-being among the psychologically healthy. Topics of interest include (but are not limited to) traditionally defined psychopathology (e.g., depression), common emotional and behavioral problems in living (e.g., conflicts in close relationships), the enhancement of subjective well-being, and the processes of psychological change in everyday life (e.g., self-regulation) and professional settings (e.g., psychotherapy and counseling). Articles reporting the results of theory-driven empirical research are given priority, but theoretical articles, review articles, clinical case studies, and essays on professional issues are also welcome. Articles describing the development of new scales (personality or otherwise) or the revision of existing scales are not appropriate for this journal.
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