Scene classification of remote sensing image based on deep network and multi-scale features fusion

Optik ◽  
2018 ◽  
Vol 171 ◽  
pp. 287-293 ◽  
Author(s):  
Zhou Yang ◽  
Xiao-dong Mu ◽  
Feng-an Zhao
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Deep Network ◽  
Features Fusion ◽  
Multi Scale

Scene classification of remote sensing image based on deep network grading transferring

Optik ◽  
2018 ◽  
Vol 168 ◽  
pp. 127-133 ◽  
Author(s):  
Zhou Yang ◽  
Xiao-dong Mu ◽  
Feng-an Zhao
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Deep Network

scholarly journals SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 681-685
Author(s):  
Y. Chen ◽  
D. Ming
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Scale ◽  
Digital Orthophoto

<p><strong>Abstract.</strong> In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.</p>

scholarly journals Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image

2020 ◽  
Vol 12 (11) ◽  
pp. 1887 ◽  
Author(s):  
Xiaolei Zhao ◽  
Jing Zhang ◽  
Jimiao Tian ◽  
Li Zhuo ◽  
Jie Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Attention ◽  
Remote Sensing Image ◽  
Learning Ability ◽  
Aerial Image ◽  
Dense Network ◽  
Scene Classification ◽  
Training Requirements

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.

scholarly journals Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (14) ◽  
pp. 2728
Author(s):  
Qingjie Zeng ◽  
Jie Geng ◽  
Kai Huang ◽  
Wen Jiang ◽  
Jun Guo
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Training Set ◽  
Shot Classification ◽  
Support Set ◽  
Feature Expression

Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.

scholarly journals Scene classification of remote sensing image based on compound pruning

2021 ◽  
Vol 336 ◽  
pp. 06030
Author(s):  
Fengbing Jiang ◽  
Fang Li ◽  
Guoliang Yang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Model Parameters ◽  
Scene Classification ◽  
Remote Sensing Image Classification ◽  
And Storage

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.

scholarly journals FEATURE FUSION FOR CROSS-MODAL SCENE CLASSIFICATION OF REMOTE SENSING IMAGE

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 63-66
Author(s):  
W. Geng ◽  
W. Zhou ◽  
S. Jin
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Aerial Image ◽  
Svm Classifier ◽  
Scene Classification ◽  
Street View ◽  
Modal Model

Abstract. Scene classification plays an important role in remote sensing field. Traditional approaches use high-resolution remote sensing images as data source to extract powerful features. Although these kind of methods are common, the model performance is severely affected by the image quality of the dataset, and the single modal (source) of images tend to cause the mission of some scene semantic information, which eventually degrade the classification accuracy. Nowadays, multi-modal remote sensing data become easy to obtain since the development of remote sensing technology. How to carry out scene classification of cross-modal data has become an interesting topic in the field. To solve the above problems, this paper proposes using feature fusion for cross-modal scene classification of remote sensing image, i.e., aerial and ground street view images, expecting to use the advantages of aerial images and ground street view data to complement each other. Our cross- modal model is based on Siamese Network. Specifically, we first train the cross-modal model by pairing different sources of data with aerial image and ground data. Then, the trained model is used to extract the deep features of the aerial and ground image pair, and the features of the two perspectives are fused to train a SVM classifier for scene classification. Our approach has been demonstrated using two public benchmark datasets, AiRound and CV-BrCT. The preliminary results show that the proposed method achieves state-of-the-art performance compared with the traditional methods, indicating that the information from ground data can contribute to aerial image classification.

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