遥感影像场景分类综述
- Remote Sensing Image Scene Classification Using CNN-CapsNet
- Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images
- Remote Sensing Scene Classification by Gated Bidirectional Network
- Robust space–frequency joint representation for remote sensing image scene classification
- Scale-free convolutional neural network for remote sensing scene classification
- A feature aggregation convolutional neural network for remote sensing scene classification
- Scene classification using hierarchical Wasserstein CNN
- Scene classification based on multiscale convolutional neural network
- When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs
- Remote sensing scene classification using multilayer stacked covariance pooling
- Fusion High-and-Low-Level Features via Ridgelet and Convolutional Neural Networks for Very High-Resolution Remote Sensing Imagery Classification
- Siamese Convolutional Neural Networks for Remote Sensing Scene Classification
- Attention Consistent Network for Remote Sensing Scene Classification
- Multilayer Feature Fusion With Weight Adjustment Based on a Convolutional Neural Network for Remote Sensing Scene Classification
- A Multi-Level Convolution Pyramid Semantic Fusion Framework for High-Resolution Remote Sensing Image Scene Classification and Annotation
- Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
- Multilayer Feature Fusion Network for Scene Classification in Remote Sensing
Remote Sensing Image Scene Classification Using CNN-CapsNet
; Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images
Remote Sensing Scene Classification by Gated Bidirectional Network
; Robust space–frequency joint representation for remote sensing image scene classification
Scale-free convolutional neural network for remote sensing scene classification
Graphical representation of the architecture of (a) original model, (b) our SF-CNN model, and © FCL convolution.
; A feature aggregation convolutional neural network for remote sensing scene classification
Overall structure of the proposed FACNN. FACNN is mainly composed of three components: backbone pipeline, convolutional feature encoding module, and softmax classifier. The convolutional feature encoding module, as shown in the orange box, is proposed to encoding the intermediate convolutional features into the convolutional representation through the end-to-end training. First, intermediate convolutional features are fed to the pooling layer to unify the width and height of different features to 7×7 . Second, the concatenation layer is used to concatenate different features by the channel. Third, the 1×1 convolutional layers with ReLU are utilized to fuse the information of different features among the channel. Then, the global average pooling layer is employed to generate the convolutional representation. The convolutional representation is merged with the second FC feature to generate the discriminative scene representation. Finally, the discriminative scene representation is fed to the softmax classifier to obtain the semantic labels of remote sensing scenes.
Scene classification using hierarchical Wasserstein CNN
Pipeline of our proposed method. The pipeline primarily involves the following steps: 1) fine-tuning three pretrained CNNs using RS training sets; 2) extracting the validation images’ deep features from three fine-tuned CNNs and concatenating them; 3) building a category tree using the concatenated features; 4) training individual HW-CNNs; and 5) conducting testing.
; Scene classification based on multiscale convolutional neural network
When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs
Illustration of the core idea of the proposed D-CNN method. To address the challenges of within-class diversity and between-class similarity, we propose to learn D-CNNs by optimizing a new objective function. Apart from minimizing the cross-entropy loss, we also impose a metric learning regularization term on the CNN features to enforce the D-CNN models to be more discriminative. Thus, in the D-CNN feature spaces, the images from the same scene class are as close as possible and the images of different classes are as far away as possible.
; Remote sensing scene classification using multilayer stacked covariance pooling
Flowchart of the proposed MSCP classification framework. The proposed framework consists of three steps: 1) multilayer feature extraction using a pretrained CNN model; 2) MSCP; and 3) SVM classification. Dotted and colored lines: downsampling and channelwise average fusion, respectively.
Fusion High-and-Low-Level Features via Ridgelet and Convolutional Neural Networks for Very High-Resolution Remote Sensing Imagery Classification
; Siamese Convolutional Neural Networks for Remote Sensing Scene Classification
Attention Consistent Network for Remote Sensing Scene Classification
; Multilayer Feature Fusion With Weight Adjustment Based on a Convolutional Neural Network for Remote Sensing Scene Classification
Framework of MLFF-WA.
Crossing access with weight adjustment.
A Multi-Level Convolution Pyramid Semantic Fusion Framework for High-Resolution Remote Sensing Image Scene Classification and Annotation
; Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
Multilayer Feature Fusion Network for Scene Classification in Remote Sensing
Original: https://blog.csdn.net/SurmountH/article/details/114647734
Author: SurmountH
Title: 遥感影像场景分类综述
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