scholarly journals Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 86 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Kurt Marfurt
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Remote Sensing Image ◽  
Natural Images ◽  
Natural Image ◽  
Scene Classification ◽  
Neural Network Models

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

Research on Remote Sensing Image Target Recognition Based on Deep Convolution Neural Network

2019 ◽  
Vol 34 (05) ◽  
pp. 2054015 ◽  
Author(s):  
Xiaofeng Han ◽  
Tao Jiang ◽  
Zifei Zhao ◽  
Zhongteng Lei
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Image Data ◽  
Network Models ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Neural Network Models ◽  
Current Mass

Target recognition is an important application in the time of high-resolution remote sensing images. However, the traditional target recognition method has the characteristics of artificial design, and the generalization ability is not strong, which makes it difficult to meet the requirement of the current mass data. Therefore, it is urgent to explore new methods for feature extraction and target recognition and location in remote sensing images. Convolutional neural network in deep learning can extract representative and discriminative multi-level features of typical features from images, so it can be used for multi-target recognition of remote sensing big data in complex scenes. In this study, NWPU VHR-10 data was selected, 50% was used for training, and the remainder was used for verification. The target recognition effects of two kinds of convolutional neural network models, Faster R-CNN and SSD, were studied and compared, and the mean average precision (mAP) was used for evaluation. The evaluation results show that the Faster R-CNN has three categories with an accuracy of more than 80%, and the SSD has seven categories with an accuracy of more than 80%, all of which show good results. The SSD model is particularly prominent in running time and recognition results, which proves convolutional neural networks have broad application prospects in the target recognition of remote sensing image data.

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 Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network

2020 ◽  
Vol 12 (23) ◽  
pp. 4003
Author(s):  
Yansheng Li ◽  
Ruixian Chen ◽  
Yongjun Zhang ◽  
Mi Zhang ◽  
Ling Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Human Beings ◽  
Scene Classification ◽  
Scene Graph ◽  
Visual Elements ◽  
Topological Relationships

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.

Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network

2021 ◽  
Vol 87 (8) ◽  
pp. 577-591
Author(s):  
Fengpeng Li ◽  
Jiabao Li ◽  
Wei Han ◽  
Ruyi Feng ◽  
Lizhe Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Data Set ◽  
Unsupervised Deep Learning

Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.

scholarly journals A Convolutional Neural Network Based on Grouping Structure for Scene Classification

2021 ◽  
Vol 13 (13) ◽  
pp. 2457
Author(s):  
Xuan Wu ◽  
Zhijie Zhang ◽  
Wanchang Zhang ◽  
Yaning Yi ◽  
Chuanrong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Remote Sensing Image ◽  
Difficult Problem ◽  
Image Features ◽  
Attention Mechanism ◽  
Scene Classification

Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable of being applied elsewhere as a plug-in model was developed. Meanwhile, a hyper-parameter C in MGCNN is introduced to probe into the influence of different grouping strategies for feature extraction. Experiments on the two selected datasets, the RSI-CB dataset and UC-Merced dataset, were carried out to verify the effectiveness of this newly proposed convolutional neural network, the accuracy obtained by MGCNN was 2% higher than the ResNet-50. An algorithm of attention mechanism was thus adopted and incorporated into grouping processes and a multiple grouped attention convolutional neural network (MGCNN-A) was therefore constructed to enhance the generalization capability of MGCNN. The additional experiments indicate that the incorporation of the attention mechanism to MGCNN slightly improved the accuracy of scene classification, but the robustness of the proposed network was enhanced considerably in remote sensing image classifications.

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