scholarly journals Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4718
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Dimensionality Reduction ◽  
Image Retrieval ◽  
High Dimensional Data ◽  
Remote Sensing Image ◽  
High Dimensional ◽  
Support Vector ◽  
Low Dimensional ◽  
Fully Connected

Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representative and discriminative features are essential to ensure good retrieval accuracy and speed. Dimensionality reduction is one of the important solutions to improve the quality of features in image retrieval, in which LargeVis is an effective algorithm specifically designed for Big Data visualization. Here, an extended LargeVis (E-LargeVis) dimensionality reduction method for high-resolution remote sensing image retrieval is proposed. This can realize the dimensionality reduction of single high-dimensional data by modeling the implicit mapping relationship between LargeVis high-dimensional data and low-dimensional data with support vector regression. An effective high-resolution remote sensing image retrieval method is proposed to obtain stronger representative and discriminative deep features. First, the fully connected layer features are extracted using a channel attention-based ResNet50 as a backbone network. Then, E-LargeVis is used to reduce the dimensionality of the fully connected features to obtain a low-dimensional discriminative representation. Finally, L2 distance is computed for similarity measurement to realize the retrieval of high-resolution remote sensing images. The experimental results on four high-resolution remote sensing image datasets, including UCM, RS19, RSSCN7, and AID, show that for various convolutional neural network architectures, the proposed E-LargeVis can effectively improve retrieval performance, far exceeding other dimensionality reduction methods.

scholarly journals Unsupervised Text Feature Learning via Deep Variational Auto-encoder

2020 ◽  
Vol 49 (3) ◽  
pp. 421-437
Author(s):  
Genggeng Liu ◽  
Lin Xie ◽  
Chi-Hua Chen
Keyword(s):  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Image Data ◽  
Original Data ◽  
Feature Representation ◽  
High Dimensional ◽  
Learning To Learn ◽  
Text Feature ◽  
Reduction Methods ◽  
Low Dimensional

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

scholarly journals RESEARCH ON HIGH RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION BASED ON SEGNET SEMANTIC MODEL IMPROVED BY GENETIC ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 415-422
Author(s):  
M. Xue ◽  
B. Wei ◽  
L. Yang
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Support Vector ◽  
Local Optimum ◽  
Semantic Model ◽  
Back Propagation Algorithm

Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.

scholarly journals Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

2019 ◽  
Vol 9 (9) ◽  
pp. 1816 ◽  
Author(s):  
Guangsheng Chen ◽  
Chao Li ◽  
Wei Wei ◽  
Weipeng Jing ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Dilated Convolution ◽  
Segmentation Task ◽  
Fully Connected ◽  
Spatial Pyramid

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

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