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Adaptive Deep Co-occurrence Feature Learning based on Classifier-Fusion for Remote Sensing Scene Classification

Ronald Tombe, Serestina Viriri
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task.  ...  forest for feature learning and classification through majority votes with ensemble classifiers.  ...  Forest Classifiers for Remote Sensing Scene Classification.  ... 
doi:10.1109/jstars.2020.3044264 fatcat:bykhvjnp7bgupf2xwudn5cxq2m


S. Jiang, H. Zhao, W. Wu, Q. Tan
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier.  ...  High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category.  ...  In the field of remote sensing scene classification, CNN models have also gradually been used.  ... 
doi:10.5194/isprs-archives-xlii-3-657-2018 fatcat:5npxqyev6vagvpvh5d7c3bq5su

NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery

Zhuang Zhou, Shengyang Li, Wei Wu, Weilong Guo, Xuan Li, Guisong Xia, Zifei Zhao
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Scene classification is one of the most important applications of remote sensing.  ...  Researchers have proposed various datasets and innovative methods for remote sensing scene classification in recent years.  ...  The dataset they proposed and codes for reproducing all the results in this article are freely available from  ... 
doi:10.1109/jstars.2021.3063096 fatcat:yffjkv46ubeznklh7zxc3ebjuq

Compact Deep Color Features for Remote Sensing Scene Classification

Rao Muhammad Anwer, Fahad Shahbaz Khan, Jorma Laaksonen
2021 Neural Processing Letters  
AbstractAerial scene classification is a challenging problem in understanding high-resolution remote sensing images.  ...  Comprehensive experiments are performed on five remote sensing scene classification benchmarks: UC-Merced with 21 scene classes, WHU-RS19 with 19 scene types, RSSCN7 with 7 categories, AID with 30 aerial  ...  Acknowledgements This work has been funded by the Academy of Finland in project AIROBEST (317388) and supported by the Aalto Science-IT project.  ... 
doi:10.1007/s11063-021-10463-4 fatcat:getvq2myhvayhbgzdzpknzenkq

Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification [chapter]

Chang Luo, Hanqiao Huang, Yong Wang, Shiqiang Wang
2018 Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure [Working Title]  
Then, the pre-trained deep CNNs with fixed parameters are transferred for remote scene classification, which solve the problem of timeconsuming and parameters over-fitting at the same time.  ...  Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks.  ...  Linear SVM is used as classifier. With various pre-trained deep CNN models and remote sensing datasets, the remote scene classification performances are shown in Table 1 .  ... 
doi:10.5772/intechopen.81982 fatcat:kqbrwh6gbrgxvdsvaeuqixm2my

Advances in Scene Classification of Remotely Sensed High Resolution Images and the Existing Datasets

With the usage of different deep learning architecture and the availability of various high resolution image datasets, the field of Remote Sensing Scene Classification of high resolution (RSSCHR) images  ...  Research on Scene classification of remotely sensed images has shown a significant improvement in the recent years as it is used in various applications such as urban planning, urban mapping, management  ...  Therefore an effective representation of features is required to develop a high performance Remote Sensing scene Classifier.  ... 
doi:10.35940/ijitee.j8841.0881019 fatcat:7dznp4cr7zfv5m25y662dskuse

A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

Mu Ye, Li Ji, Luo Tianye, Li Sihan, Zhang Tong, Feng Ruilong, Hu Tianli, Gong He, Guo Ying, Sun Yu, Thobela Louis Tyasi, Li Shijun
2022 Computers Materials & Continua  
In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images.  ...  Therefore, the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.  ...  Therefore, except for good applicability in the fine classification of high-precision and low-precision remote sensing data, the model design by us can also classify fuzzy images, recognize and classify  ... 
doi:10.32604/cmc.2022.026880 fatcat:upicgfdfavhgtkwjtw5jdtduiy

Fusion of Deep Learning Models for Improving Classification Accuracy of Remote Sensing Images

P Deepan
The proposed approach is validated with 7,000 remote sensing images from Northern Western Polytechnical University -Remote Sensing Image Scene Classification (NWPU-RESISC) 45 class dataset and confirmed  ...  The intent of this paper is to study the effect of ensemble classifier constructed by combining three Deep Convolutional Neural Networks (DCNN) namely; CNN, VGG-16 and Res Inception models by using average  ...  [XIX] presented an approach that compared land cover classification of remote sensing images with non-parametric classifier such as SVM, k-NN and random forest algorithms.  ... 
doi:10.26782/jmcms.2019.10.00015 fatcat:4caqx5u5ezgr5hehsxtvczun44

Learning Multi-Granularity Neural Network Encoding Image Classification Using DCNNs for Easter Africa Community Countries

Musabe Jean Bosco, Guoyin Wang, Yves Hategekimana
2021 IEEE Access  
In our experiments, we attempted to fine-tune the deep convolutional neural networks (DCNNs) training method for remote sensing scene classification on two public datasets UCM, SIRI-WHU, and one dataset  ...  INDEX TERMS Convolutional neural networks (CNNs), fine-tuning, granularity feature extraction, machine learning, and remote sensing (RS).  ...  AUTHOR CONTRIBUTIONS Conceptualization, Jean Bosco Musabe and Wang Guoyin; Data curation and Jean Bosco Musabe; Formal analysis, Jean Bosco Musabe, Wang Guoyin, and Yves Hategekimana; Methodology, Jean  ... 
doi:10.1109/access.2021.3122569 fatcat:arizvsrcnjbxxlclzrcvtep42a

Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework

Xingrui Yu, Xiaomin Wu, Chunbo Luo, Peng Ren
2017 GIScience & Remote Sensing  
remote sensing scene classification, empowered by deep learning.  ...  enhanced datasets to train a deep convolutional neural network (CNN) that achieves state-of-the-art scene classification performance.  ...  We use the CNN structure illustrated in Figure 1 for remote sensing scene classification. Here a remote sensing image is one input of the convolutional neural network.  ... 
doi:10.1080/15481603.2017.1323377 fatcat:fm54suan5nhhthnvi4nu2qebvu

A New Method for Scene Classification from the Remote Sensing Images

Purnachand Kollapudi, Saleh Alghamdi, Neenavath Veeraiah, Youseef Alotaibi, Sushma Thotakura, Abdulmajeed Alsufyani
2022 Computers Materials & Continua  
Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research.  ...  This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization.  ...  An acronym for Remote Sensing Land-Use Scene Categorization (BOVW) has been useful in classification of remote sensing images of land-use scenes, which have been a particularly excellent use of the BOVW  ... 
doi:10.32604/cmc.2022.025118 fatcat:wnpbnmsirjem5mp3xukmtcp2fa

Classification using semantic feature and machine learning: Land-use case application

Hela Elmannai, Abeer Dhafer AlGarni
2021 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
The presented paper deals with the land-use scene recognition on very high-resolution remote sensing imagery.  ...  The method starts by semantic feature extraction using a convolutional neural network. Handcraft features are also extracted based on color and multi-resolution characteristics.  ...  imagery classification Semantic features extracted form deep convolutional neural networks [13] different CNN-based models remote sensing land use classification Transfer learning using ImageNet dataset  ... 
doi:10.12928/telkomnika.v19i4.18359 fatcat:kzfvhmhtvfclnhxzaf7lqx6xka

A Multi-Scale Approach for Remote Sensing Scene Classification Based on Feature Maps Selection and Region Representation

Jun Zhang, Min Zhang, Lukui Shi, Wenjie Yan, Bin Pan
2019 Remote Sensing  
In general, most researchers directly use raw deep features extracted from the convolutional networks to classify scenes.  ...  Recently, deep convolutional neural networks have presented promising performance in high-resolution remote sensing scene classification research.  ...  providing the UCM, AID and NWPU datasets in their study, respectively.  ... 
doi:10.3390/rs11212504 fatcat:2mpyc7dzibhkthoshipu2clkou

Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model

P. Deepan, L. Sudha, K. Kalaivani, J. Ganesh
2022 EAI Endorsed Transactions on Scalable Information Systems  
Image Scene Classification (RSISC-16 Net) deep learning model for scene classification.  ...  A wide variety of deep learning models have emerged for the task of scene classification in remote sensing image analysis. The majority of these models have shown significant success.  ...  The main motivation of our proposed work is to develop remote sensing scene image classification model using deep learning [21, 22] to extract features automatically and to classify the scenes accurately  ... 
doi:10.4108/eai.1-2-2022.173292 fatcat:hams7xrkyjdlboofxecbdjgqme

Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification [article]

Qingshan Liu, Renlong Hang, Huihui Song, Fuping Zhu, Javier Plaza
2016 arXiv   pre-print
In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification.  ...  Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification  ...  CONCLUSIONS This paper introduces a new adaptive deep pyramid matching (ADPM) model intended to properly fuse convolutionallayer features in classification of remotely sensed images using convolutional  ... 
arXiv:1611.03589v1 fatcat:mnonbyz5fvcavl65g6qnhoyd4y
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