scholarly journals Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (18) ◽  
pp. 3579
Author(s):  
Junge Shen ◽  
Chi Zhang ◽  
Yu Zheng ◽  
Ruxin Wang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Adaptation Effect ◽  
Scene Classification ◽  
Feature Maps ◽  
Decision Level ◽  
Importance Factor ◽  
Decision Level Fusion ◽  
Level Fusion

Remote sensing image scene classification acts as an important task in remote sensing image applications, which benefits from the pleasing performance brought by deep convolution neural networks (CNNs). When applying deep models in this task, the challenges are, on one hand, that the targets with highly different scales may exist in the image simultaneously and the small targets could be lost in the deep feature maps of CNNs; and on the other hand, the remote sensing image data exhibits the properties of high inter-class similarity and high intra-class variance. Both factors could limit the performance of the deep models, which motivates us to develop an adaptive decision-level information fusion framework that can incorporate with any CNN backbones. Specifically, given a CNN backbone that predicts multiple classification scores based on the feature maps of different layers, we develop a pluginable importance factor generator that aims at predicting a factor for each score. The factors measure how confident the scores in different layers are with respect to the final output. Formally, the final score is obtained by a class-wise and weighted summation based on the scores and the corresponding factors. To reduce the co-adaptation effect among the scores of different layers, we propose a stochastic decision-level fusion training strategy that enables each classification score to randomly participate in the decision-level fusion. Experiments on four popular datasets including the UC Merced Land-Use dataset, the RSSCN 7 dataset, the AID dataset, and the NWPU-RESISC 45 dataset demonstrate the superiority of the proposed method over other state-of-the-art methods.

scholarly journals REMOTE SENSING SCENE CLASSIFICATION USING MULTIPLE PYRAMID POOLING

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 279-284
Author(s):  
Y. Yao ◽  
H. Zhao ◽  
D. Huang ◽  
Q. Tan
Keyword(s):  
Remote Sensing ◽  
Spatial Relationship ◽  
Image Data ◽  
Remote Sensing Image ◽  
Input Image ◽  
Image Size ◽  
Scene Classification ◽  
Fixed Size ◽  
Fixed Length ◽  
Fully Connected

<p><strong>Abstract.</strong> Remote sensing image scene classification has gained remarkable attention, due to its versatile use in different applications like geospatial object detection, ground object information extraction, environment monitoring and etc. The scene not only contains the information of the ground objects, but also includes the spatial relationship between the ground objects and the environment. With rapid growth of the amount of remote sensing image data, the need for automatic annotation methods for image scenes is more urgent. This paper proposes a new framework for high resolution remote sensing images scene classification based on convolutional neural network. To eliminate the requirement of fixed-size input image, multiple pyramid pooling strategy is equipped between convolutional layers and fully connected layers. Then, the fixed-size features generated by multiple pyramid pooling layer was extended to one-dimension fixed-length vector and fed into fully connected layers. Our method could generate a fixed-length representation regardless of image size, at the same time get higher classification accuracy. On UC-Merced and NWPU-RESISC45 datasets, our framework achieved satisfying accuracies, which is 93.24% and 88.62% respectively.</p>

scholarly journals AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification

2020 ◽  
Vol 12 (24) ◽  
pp. 4046
Author(s):  
Xirong Li ◽  
Fangling Pu ◽  
Rui Yang ◽  
Rong Gui ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Remote Sensing Image ◽  
Similarity Measurement ◽  
Scene Classification ◽  
Feature Maps ◽  
Training Strategy ◽  
Measurement Results ◽  
Classification Tasks ◽  
The Cost

In recent years, deep neural network (DNN) based scene classification methods have achieved promising performance. However, the data-driven training strategy requires a large number of labeled samples, making the DNN-based methods unable to solve the scene classification problem in the case of a small number of labeled images. As the number and variety of scene images continue to grow, the cost and difficulty of manual annotation also increase. Therefore, it is significant to deal with the scene classification problem with only a few labeled samples. In this paper, we propose an attention metric network (AMN) in the framework of the few-shot learning (FSL) to improve the performance of one-shot scene classification. AMN is composed of a self-attention embedding network (SAEN) and a cross-attention metric network (CAMN). In SAEN, we adopt the spatial attention and the channel attention of feature maps to obtain abundant features of scene images. In CAMN, we propose a novel cross-attention mechanism which can highlight the features that are more concerned about different categories, and improve the similarity measurement performance. A loss function combining mean square error (MSE) loss with multi-class N-pair loss is developed, which helps to promote the intra-class similarity and inter-class variance of embedding features, and also improve the similarity measurement results. Experiments on the NWPU-RESISC45 dataset and the RSD-WHU46 dataset demonstrate that our method achieves the state-of-the-art results on one-shot remote sensing image scene classification tasks.

scholarly journals An Attention-Guided Multilayer Feature Aggregation Network for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3113
Author(s):  
Ming Li ◽  
Lin Lei ◽  
Yuqi Tang ◽  
Yuli Sun ◽  
Gangyao Kuang
Keyword(s):  
Remote Sensing ◽  
Feature Learning ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Learning Ability ◽  
Scene Classification ◽  
Feature Maps ◽  
Feature Aggregation ◽  
Scene Representation ◽  
High Level

Remote sensing image scene classification (RSISC) has broad application prospects, but related challenges still exist and urgently need to be addressed. One of the most important challenges is how to learn a strong discriminative scene representation. Recently, convolutional neural networks (CNNs) have shown great potential in RSISC due to their powerful feature learning ability; however, their performance may be restricted by the complexity of remote sensing images, such as spatial layout, varying scales, complex backgrounds, category diversity, etc. In this paper, we propose an attention-guided multilayer feature aggregation network (AGMFA-Net) that attempts to improve the scene classification performance by effectively aggregating features from different layers. Specifically, to reduce the discrepancies between different layers, we employed the channel–spatial attention on multiple high-level convolutional feature maps to capture more accurately semantic regions that correspond to the content of the given scene. Then, we utilized the learned semantic regions as guidance to aggregate the valuable information from multilayer convolutional features, so as to achieve stronger scene features for classification. Experimental results on three remote sensing scene datasets indicated that our approach achieved competitive classification performance in comparison to the baselines and other state-of-the-art methods.

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