为解决遥感图像难分类问题,本文提出一种结合 CNN 与 Transformer 优点的图像分类 方法。翻译成英文
时间: 2023-09-09 20:04:29 浏览: 81
### 回答1:
In order to solve the problem of difficult classification of remote sensing images, this paper proposes an image classification method combining the advantages of CNN and Transformer.
### 回答2:
This paper proposes an image classification method that combines the advantages of CNN and Transformer to solve the problem of difficult classification of remote sensing images.
Convolutional Neural Networks (CNN) have been widely used in image classification tasks, but they may struggle with remote sensing images due to their large size and complex features. On the other hand, Transformer, a powerful sequence-to-sequence model originally designed for natural language processing, has shown excellent performance in capturing long-range dependencies and modeling complex relationships.
To leverage the strengths of both models, this paper proposes a novel approach that combines CNN and Transformer. Firstly, the remote sensing image is fed into a CNN backbone to extract local features. The CNN backbone consists of multiple convolutional layers and pooling layers, and it effectively captures the low-level spatial information of the image.
Next, the output of the CNN backbone is transformed into a sequence of feature vectors. Each feature vector represents a local region in the remote sensing image. These feature vectors are then fed into a Transformer encoder to capture global contextual information and model the dependencies between local regions.
Finally, the transformed feature sequence is fed into a classification head, which consists of fully connected layers, to predict the class label of the remote sensing image.
Experimental results demonstrate that the proposed method outperforms traditional CNN-based methods and achieves state-of-the-art performance on remote sensing image classification tasks. By combining the local feature extraction capability of CNN and the global contextual modeling capability of Transformer, the proposed method effectively addresses the issue of difficult classification of remote sensing images.
In conclusion, this paper presents a novel image classification method that combines the advantages of CNN and Transformer to improve the classification accuracy of remote sensing images.
### 回答3:
To address the problem of difficult classification of remote sensing images, this paper proposes an image classification method that combines the advantages of CNN and Transformer.
The proposed method utilizes the strengths of both CNN and Transformer models for improved image classification accuracy. CNN (Convolutional Neural Network) is renowned for its ability to extract spatial features from an image through convolutional layers. It has been widely used in image classification tasks, including remote sensing images. However, CNNs may struggle with capturing long-range dependencies and global contextual information in images.
To overcome this limitation, the Transformer model is introduced to complement the CNN. Transformers excel in capturing global information and modeling long-range dependencies by employing self-attention mechanisms. By integrating the two models, the proposed method can effectively exploit both local details and global context of remote sensing images for classification.
In this method, the input remote sensing images are first processed by CNN layers to extract local features and preserve the spatial structure. The output features are then fed into a Transformer encoder, where the global information and long-range dependencies are captured. The final classification results are obtained by a fully connected layer and a softmax activation function.
Experimental results on large-scale remote sensing image datasets demonstrate the effectiveness of the proposed method. It achieves higher classification accuracy compared to traditional CNN-based methods. Additionally, it shows superior capability in handling complex scenes and fine-grained features in remote sensing images.
In conclusion, the proposed method combining the advantages of CNN and Transformer models offers a promising solution to address the challenging classification issue in remote sensing images. It effectively integrates local and global information for improved accuracy in image classification.