Multi-label semantic feature fusion for remote sensing image captioning

2022 ◽  
Vol 184 ◽  
pp. 1-18
Author(s):  
Shuang Wang ◽  
Xiutiao Ye ◽  
Yu Gu ◽  
Jihui Wang ◽  
Yun Meng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Semantic Feature ◽  
Image Captioning

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

scholarly journals Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4673-4687
Author(s):  
Jixiang Zhao ◽  
Shanwei Liu ◽  
Jianhua Wan ◽  
Muhammad Yasir ◽  
Huayu Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Evidence Theory ◽  
Remote Sensing Image

Cross-Modal Feature Fusion Retrieval for Remote Sensing Image-Voice Retrieval

2021 ◽  
Author(s):  
Rui Yang ◽  
Yu Gu ◽  
Yu Liao ◽  
Huan Zhang ◽  
Yingzhi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Remote Sensing Image

Multimodal Remote Sensing Image Classification with Small Sample Size Based on High-Level Feature Fusion

2019 ◽  
Vol 56 (11) ◽  
pp. 111001
Author(s):  
贺琪 Qi He ◽  
李瑶 Yao Li ◽  
宋巍 Wei Song ◽  
黄冬梅 Dongmei Huang ◽  
何盛琪 Shengqi He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sample Size ◽  
Image Classification ◽  
Feature Fusion ◽  
Small Sample Size ◽  
Remote Sensing Image ◽  
Small Sample ◽  
Remote Sensing Image Classification ◽  
High Level ◽  
High Level Feature

scholarly journals An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1999 ◽  
Author(s):  
Donghang Yu ◽  
Qing Xu ◽  
Haitao Guo ◽  
Chuan Zhao ◽  
Yuzhun Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Visual Recognition ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Image Features ◽  
Training Dataset ◽  
Scene Classification

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

scholarly journals VAA: Visual Aligning Attention Model for Remote Sensing Image Captioning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137355-137364 ◽  
Author(s):  
Zhengyuan Zhang ◽  
Wenkai Zhang ◽  
Wenhui Diao ◽  
Menglong Yan ◽  
Xin Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Captioning ◽  
Attention Model

scholarly journals WSF-NET: Weakly Supervised Feature-Fusion Network for Binary Segmentation in Remote Sensing Image

2018 ◽  
Vol 10 (12) ◽  
pp. 1970 ◽  
Author(s):  
Kun Fu ◽  
Wanxuan Lu ◽  
Wenhui Diao ◽  
Menglong Yan ◽  
Hao Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Class Imbalance ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Training Strategy ◽  
Binary Segmentation ◽  
Supervised Methods ◽  
Weakly Supervised ◽  
The Given

Binary segmentation in remote sensing aims to obtain binary prediction mask classifying each pixel in the given image. Deep learning methods have shown outstanding performance in this task. These existing methods in fully supervised manner need massive high-quality datasets with manual pixel-level annotations. However, the annotations are generally expensive and sometimes unreliable. Recently, using only image-level annotations, weakly supervised methods have proven to be effective in natural imagery, which significantly reduce the dependence on manual fine labeling. In this paper, we review existing methods and propose a novel weakly supervised binary segmentation framework, which is capable of addressing the issue of class imbalance via a balanced binary training strategy. Besides, a weakly supervised feature-fusion network (WSF-Net) is introduced to adapt to the unique characteristics of objects in remote sensing image. The experiments were implemented on two challenging remote sensing datasets: Water dataset and Cloud dataset. Water dataset is acquired by Google Earth with a resolution of 0.5 m, and Cloud dataset is acquired by Gaofen-1 satellite with a resolution of 16 m. The results demonstrate that using only image-level annotations, our method can achieve comparable results to fully supervised methods.

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