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Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

Adriana Romero, Carlo Gatta, Gustau Camps-Valls
2016 IEEE Transactions on Geoscience and Remote Sensing  
This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis.  ...  Therefore, we propose the use of greedy layer-wise unsupervised pre-training coupled with a highly efficient algorithm for unsupervised learning of sparse features.  ...  Diane Whited at the University of Montana for the VHR imagery used in some experiments of this paper.  ... 
doi:10.1109/tgrs.2015.2478379 fatcat:sirgcd47m5fdralgdeea5zgc3u

Remote Sensing Image Land Classification Based on Deep Learning

Kai Zhang, Chengquan Hu, Hang Yu, Ahmed Farouk
2021 Scientific Programming  
Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based  ...  Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realize the fusion of remote sensing  ...  And remote sensing image classification is of great significance for obtaining image information.  ... 
doi:10.1155/2021/6203444 fatcat:c3j7dkyqezduhdcmhjgejcefkq

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Yating Gu, Yantian Wang, Yansheng Li
2019 Applied Sciences  
RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection.  ...  As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years.  ...  Unsupervised Deep Feature Learning for Remote Sensing Image Scene Classification Deep neural networks [8] have been widely used to learn low-dimensional feature representations to reduce the dimensionality  ... 
doi:10.3390/app9102110 fatcat:oj3acgbmwnhzppxvvjbsn5cfzq

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]  
However, for the task of remote scene classification, there are no sufficient images to train a very deep CNN from scratch.  ...  This chapter also provides baseline for applying deep CNNs to other remote sensing tasks.  ...  For the strategy of transferring deep CNNs for remote scene classification, we use the five pretrained deep CNNs to extract high-level features from input images.  ... 
doi:10.5772/intechopen.81982 fatcat:kqbrwh6gbrgxvdsvaeuqixm2my

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Gong Cheng, Junwei Han, Xiaoqiang Lu
2017 Proceedings of the IEEE  
During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images.  ...  Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention.  ...  classification using remote sensing images.  ... 
doi:10.1109/jproc.2017.2675998 fatcat:szqrkysja5ffznxn2fq7vgo6j4

An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation [article]

Zhiqiang Gong, Ping Zhong, Weidong Hu, Fang Liu, Bingwei Hui
2019 arXiv   pre-print
This work develops a novel end-to-end deep unsupervised learning method based on convolutional neural network (CNN) with pseudo-classes for remote sensing scene representation.  ...  Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation  ...  Flowchart of the proposed method for unsupervised learning of remote sensing scenes. The CNN model is used to extract deep features from the remote sensing scenes.  ... 
arXiv:1903.07224v1 fatcat:rltb2nhmjfhm5et2yynrsk34ke

PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval

Weixun Zhou, Shawn Newsam, Congmin Li, Zhenfeng Shao
2018 ISPRS journal of photogrammetry and remote sensing (Print)  
Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data of interest from large collections of remote sensing data, is a fundamental task in remote sensing.  ...  These limitations have severely restricted the development of novel feature representations for RSIR, particularly the recent deep-learning based features which require large amounts of training data.  ...  Acknowledgements The authors would like to thank Paolo Napoletano for the code used in the performance evaluation.  ... 
doi:10.1016/j.isprsjprs.2018.01.004 fatcat:v5oei4amy5a4nbqklertcg74lm

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities [article]

Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo, Gui-Song Xia
2020 arXiv   pre-print
Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant  ...  However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking.  ...  data sets for remote sensing image scene classification.  ... 
arXiv:2005.01094v1 fatcat:qz3at3gyvrbtzkluumalvpqb64

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Hu 2579 Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast ...............................................  ...  Salzmann 2448 Adaptive Spectral-Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification ........... .....................................................................  ... 
doi:10.1109/tgrs.2021.3052119 fatcat:obk5h6sp2nh47ounq4jqlhukcu

Deep Learning for Remote Sensing Image Understanding

Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, Xue Cheng Tai
2016 Journal of Sensors  
Lv et al. introduces deep belief networks to extract effective contextual mapping features for the task of PolSAR image classification. The paper by W.  ...  Papers in category (a) deal with the classic classification problem for distinct types of remote sensing images. The paper by Z.  ...  We appreciate all the authors for their submissions, as well as all the reviewers for their careful and professional review.  ... 
doi:10.1155/2016/7954154 fatcat:yoyzkgdi25er5hqggp4qub7plu


Z. Nordin, H. Z. M. Shafri, A. F. Abdullah, S. J. Hashim
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Thus, this paper reviews current techniques and future trends of multi-sources Remote Sensing for building extraction.  ...  coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data.  ...  The used of Deep Learning method in Remote Sensing image has been preferred to extract the object for many purposes.  ... 
doi:10.5194/isprs-archives-xlii-4-w16-489-2019 fatcat:xhlrmiru5reo5fbqkhafxucl6a

Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network [article]

Mohbat Tharani, Numan Khurshid, Murtaza Taj
2018 arXiv   pre-print
Resolving this, we propose an unsupervised encoder-decoder feature for remote sensing image matching (RSIM).  ...  Image apprehension lately carried out by hand-crafted features in the latent space have been replaced by deep features acquired from supervised networks for improved understanding.  ...  Recently, autoencoder based unsupervised methods for feature extraction have been used for classification of diverse set of images including remote sensing images [19, 12] .  ... 
arXiv:1810.06470v1 fatcat:httf7slohnhcphs6ggbtzi37yy

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
sensing; one paper on domain adaptation; and one paper on feature extraction methods.  ...  processing; two papers utilizing spectral-spatial processing for hyperspectral image analysis; three papers on object tracking and recognition; one paper studying how deep networks need to be for remote  ...  Domain Adaptation Ma et al. in "Deep neural network-based domain adaptation for classification of remote sensing images" utilizes class centroid alignment is used for unsupervised domain adaptation (assuming  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

State-of-the-art and gaps for deep learning on limited training data in remote sensing [article]

John E. Ball, Derek T. Anderson, Pan Wei
2018 arXiv   pre-print
Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled.  ...  ., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data.  ...  remote sensing.  ... 
arXiv:1807.11573v1 fatcat:q6vtrod6nvgtrihjafo25iz3wi

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Liangpei Zhang, Lefei Zhang, Bo Du
2016 IEEE Geoscience and Remote Sensing Magazine  
for pixel-based classification.  ...  preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.  ...  He was session chair for the Fourth IEEE Geoscience and Remote Sensing Society Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.  ... 
doi:10.1109/mgrs.2016.2540798 fatcat:yuqd4e2bm5bijbrssdxmu2zxcq
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