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Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint
2021
Remote Sensing
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In recent years, many convolutional neural network (CNN)-based methods have been proposed to address the scene classification tasks of remote sensing images. ...
However, the augmented samples increase the intra-class diversity of the training set, which is a challenge to complete the following classification process. ...
• We combine the label augmentation and intra-class constraint to further improve the remote sensing image classification accuracy. ...
doi:10.3390/rs13132566
fatcat:n7usr2pf6jgnzakcajy4pwl6hu
A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification
2021
Remote Sensing
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Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. ...
Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. ...
Moreover, the characteristics of remote sensing images state that the scenes have large intra-class diversity and large inter-class similarity. ...
doi:10.3390/rs13030433
fatcat:pbtqqxw77bc7da2xhmj6szdv2e
BEYOND HAND-CRAFTED FEATURES IN REMOTE SENSING
2013
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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A basic problem of image classification in remote sensing is to select suitable image features. However, modern classifiers such as AdaBoost allow for feature selection driven by the training data. ...
To be able to efficiently extract a large quasi-exhaustive set of multi-scale texture and intensity features we suggest to approximate standard derivative filters via integral images. ...
INTRODUCTION Automated classification of remotely sensed images is one of the fundamental challenges in remote sensing research. ...
doi:10.5194/isprsannals-ii-3-w1-35-2013
fatcat:b7cafolxh5atpkm3vd4rcgyjgm
Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network
2017
Remote Sensing
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In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. ...
Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral "distance" between remote sensing images and images used to pre-train deep CNNs. ...
Remote Sens. 2017, 9, 225 ...
doi:10.3390/rs9030225
fatcat:ghpzuij5uzavhh7aaxbqfkxlra
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
2021
Sensors
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Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide significant value in land use and land cover (LULC) classification. ...
The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. ...
Another constraint with remote sensing images was the presence of scenic variability, which limited the classification performance. ...
doi:10.3390/s21238083
pmid:34884087
fatcat:v4pys3s2xnbkhi3w46glrjuixq
Unlocking the capabilities of explainable fewshot learning in remote sensing
[article]
2023
arXiv
pre-print
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However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. ...
We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. ...
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Civil Aviation Authority of Singapore. ...
arXiv:2310.08619v1
fatcat:76qh4smd5jhcdg5nrcsrkhuxlq
Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification
2020
Remote Sensing
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Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. ...
In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. ...
Author Contributions: Cheng Shi was primarily responsible for the original idea and experimental design. Zhiyong Lv and Xiuhong Yang contributed to the experimental analysis. ...
doi:10.3390/rs12061012
fatcat:togwebcqmrevzaakglls7lruga
Extended Subspace Projection upon Sample Augmentation based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. ...
and ignore the augmentation of training samples. ...
With the high spectral resolution, hyperspectral remote sensing image (HSI) enables a complete spectral diagnosis of ground objects, thus allowing a fine classification of land cover and land use classes ...
doi:10.1109/jstars.2021.3107105
fatcat:kkmidx3vcvbvdbx7hwuvo7qpfm
Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge
2015
Remote Sensing
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graph-based spectral clustering algorithm and pairwise constraints that are incrementally added via active learning. ...
Experiments on several datasets of remote sensing images show that our algorithm achieves state-of-the-art performance in the annotation of remote sensing images and demonstrates high potential in many ...
Acknowledgments This research was supported by the National Natural Science Foundation of China under grants No. 91338113 and No. 41501462, and was partially funded by the Wuhan Municipal Science and Technology ...
doi:10.3390/rs71115014
fatcat:jwbzycqrgnhr3jwiiqrlp43zae
Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning
2020
IEEE Geoscience and Remote Sensing Letters
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2020 pre-print arXiv:2001.01425v1 fatcat:dcc5ur2zdra43mtxlu3zvfdnu4
in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. ...
The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting ...
ACKNOWLEDGMENT We thank the TerraSAR-X Science Service System for the provision of images (Proposals MTH-1118 and LAN-3156), and University of Chinese Academy of Sciences (UCAS) Joint PhD Training Program ...
doi:10.1109/lgrs.2020.2965558
fatcat:tijgsgw2bfhzrcjk6jpakljray
Habitat classification from satellite observations with sparse annotations
[article]
2022
arXiv
pre-print
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We show that cropping augmentations, test-time augmentation and semi-supervised learning can help classification even further. ...
Usually training data used for automatic land cover classification relies on fully annotated segmentation maps, annotated from remote sensed imagery to a fairly high-level taxonomy, i.e., classes such ...
Gaussian blur is a suitable augmentation for remote sensing data because the value ranges can be arbitrary and the blurred scene is still a valid representation of the underlying true natural scene. ...
arXiv:2209.12995v1
fatcat:n6yzji5msrhgjd3jsjpn53nniy
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data
2022
Remote Sensing
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However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. ...
We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most ...
In addition, the Augmented Associative Learning-based (AAL-based) domain adaptation network [164] achieved the hyper-spectral remote sensing image classification by utilizing source classification loss ...
doi:10.3390/rs14174380
fatcat:4o6kc2jrwvaupcpehpdza7deou
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
2020
Remote Sensing
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pairs and triplets based on the supervised information (e.g., class labels). ...
Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based ...
Introduction Nowadays, the increasing availability of remote sensing (RS) data offers widespread opportunities in many important application fields, such as urban planning [1] [2] [3] , aerial scene retrieval ...
doi:10.3390/rs12162603
fatcat:2khmmy67vjbtnj7cct7jacngpe
Virtual Hyperspectral Images and Spectral Feature Extraction Using Symmetric Autoencoders
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2309.14286v2 fatcat:ptoknhdm5jeoliknfidzwuvbbqOther Versions
Spectral data acquired through remote sensing are invaluable for environmental and resource studies. ...
The extraction of invariant spectral features within classes, enabled by SymAE, also positions it as highly effective in clustering and classification tasks. ...
Deep learning algorithms are popular for remote-sensing image analysis tasks such as image fusion, registration, scene classification, semantic segmentation, and pixel-based classification [13] . ...
arXiv:2309.14286v5
fatcat:gahxo54utvakzmgd3o6njlarvu
Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval
2020
IEEE Transactions on Geoscience and Remote Sensing
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Owing to the proliferation of large-scale remote sensing (RS) archives with multiple annotations, multi-label RS scene classification and retrieval are becoming increasingly popular. ...
To fill this gap, we propose a new graph relation network (GRN) for multi-label RS scene categorization. ...
ACKNOWLEDGMENT The authors would like to thank the authors for their efforts in creating the multi-label datasets based on UCM, AID and DFC15, and the reviewers for their valuable suggestions. ...
doi:10.1109/tgrs.2020.3016020
fatcat:qrjfmxi5vfe2hldfbu5hf5ytaq
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