The Experts below are selected from a list of 65082 Experts worldwide ranked by ideXlab platform

Hu Zhu - One of the best experts on this subject based on the ideXlab platform.

  • Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks
    IEEE Transactions on Geoscience and Remote Sensing, 2018
    Co-Authors: Yansheng Li, Yongjun Zhang, Xin Huang, Hu Zhu
    Abstract:

    As one of the most challenging tasks of Remote Sensing big data mining, large-scale Remote Sensing Image retrieval has attracted increasing attention from researchers. Existing large-scale Remote Sensing Image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale Remote Sensing Image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative Remote Sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public Remote Sensing Image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed Remote Sensing Image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.

Peng Yu-lin - One of the best experts on this subject based on the ideXlab platform.

  • Application of Support Vector Machine in Remote Sensing Image Analysis and Processing
    Communications Technology, 2008
    Co-Authors: Peng Yu-lin
    Abstract:

    It is not easy to analyze and process Remote Sensing Image due to the characters of its multiple sensors,platforms,time-dimensions,spectrums,resolutions and mass data.Support vector machine(SVM),as a new method of machine learning,is able to effectively solve these problems.By combining the features of Remote Sensing Image and Support Vector Machine,the applications of Support Vector Machine in Remote Sensing Image classification,Remote Sensing Image Compression,and Remote Sensing Image features extraction are emphatically analyzed.The paper discusses the application trend of support vector machine in Remote Sensing Image analysis and processing and the problems demanding further study.

Song Gao - One of the best experts on this subject based on the ideXlab platform.

  • Remote Sensing Image Retrieval Based on Color and Texture
    Lecture Notes in Electrical Engineering, 2013
    Co-Authors: Qinkun Xiao, Mina Liu, Song Gao
    Abstract:

    Remote Sensing Image retrieval based on texture features and color features is proposed, for meeting the limitations of single features of Remote Sensing Image retrieval and the computational cost of traditional retrieval methods. On the analysis of the existing Remote Sensing Image retrieval, the general frame of Remote Sensing Image retrieval which is based on the color and Gabor wavelet texture features are established. According to the optimization of filter parameters which designed a group of multi-scale and multi-directional filters, the two Image feature fusions are based on texture feature and color feature. Then Image retrieval prototype system is designed and implemented based on color and texture features. The obtained color and texture features are used to retrieve Image database. The experimental results show that the proposed method is efficient.

Yansheng Li - One of the best experts on this subject based on the ideXlab platform.

  • Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks
    IEEE Transactions on Geoscience and Remote Sensing, 2018
    Co-Authors: Yansheng Li, Yongjun Zhang, Xin Huang, Hu Zhu
    Abstract:

    As one of the most challenging tasks of Remote Sensing big data mining, large-scale Remote Sensing Image retrieval has attracted increasing attention from researchers. Existing large-scale Remote Sensing Image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale Remote Sensing Image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative Remote Sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public Remote Sensing Image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed Remote Sensing Image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions.

Jia Shao-hui - One of the best experts on this subject based on the ideXlab platform.

  • UAV Remote Sensing Image Fusion Based on POS System
    Remote Sensing Information, 2013
    Co-Authors: Jia Shao-hui
    Abstract:

    Since UAV Remote Sensing Images have the characteristics of small picture,large geometric distortion,and low precision Images registration,this paper proposed to adopt the method of the optimal stitching line for UAV Remote Sensing Images fusion.In the experiment,30pieces of UAV Remote Sensing Image sequence were tested,and the result showed that the method is effective for UAV Remote Sensing Image fusion,which can not only eliminate splice line,but also avoid the"ghost phenomenon".