Three dimensional Deep Learning approach for remote sensing image classification.

Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. ... Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach ... DEEP LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION The content of satellite images with high resolution in both space and frequencies is remarkably complex, providing details of objects like houses, ...

doi:10.1109/tgrs.2018.2818945 fatcat:r773oaqn6raqnguk4p7d2jjnlm

Multiple Versions

Networks (DNN) for remote sensing image classification. ... 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 ... [I] analyzed various image classification methods for remote sensing images. ...

doi:10.26782/jmcms.2019.10.00015 fatcat:4caqx5u5ezgr5hehsxtvczun44

Open Access

With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and shown powerful ability in image representation. ... We conducted extensive experiments on remote sensing image classification and retrieval tasks based on the proposed region-wise deep feature extraction framework. ... For example, a saliency-guided unsupervised feature learning approach was proposed in [19] for remote sensing scene classification. ...

doi:10.3390/rs10060871 fatcat:h5cn7zi3gzhgrhwagj4pyfbyu4

DOAJ Szczepanski

AbstractAerial scene classification is a challenging problem in understanding high-resolution remote sensing images. ... However, the importance of color within the deep learning framework is yet to be investigated for aerial scene classification. ... The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. ...

doi:10.1007/s11063-021-10463-4 fatcat:getvq2myhvayhbgzdzpknzenkq

Open Access

Zaouali et al. integrate three-dimensional (3-D) shearlet transforms with Joint Sparse Representation for hyperspectral classification. ... Mukherjee et al. present a spatially constrained angular subspace learning approach for hyperspectral image classification. ... Zaouali et al. integrate three-dimensional (3-D) shearlet transforms with Joint Sparse Representation for hyperspectral classification. ...

doi:10.1109/jstars.2018.2820938 fatcat:pqu6zhrl3rc3tm7tqpi4p4t34m

DOAJ

Thus, this paper reviews current techniques and future trends of multi-sources Remote Sensing for building extraction. ... Learning, Machine Learning approach especially in building extraction for topographic mapping and urban planning and development. ... 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

DOAJ

Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. ... Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. ... INTRODUCTION Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Hyperspectral classification is one of the foremost tasks in remote sensing image analysis. ...

doi:10.5194/isprs-archives-xlii-3-897-2018 fatcat:pmdbweglcvc4vkihkwrjovwwmy

DOAJ

The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. ... The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. ... The authors in [3] focused on Big data and machine learning for crop protection. ...

doi:10.1109/access.2021.3051196 fatcat:hewivbzua5a27jotazlmqvps7i

DOAJ

Remote sensing image scene classification has gained remarkable attention because of its versatile use in different applications like geospatial object detection, natural hazards detection, geographic ... We have experimented on a recently proposed NWPU-RESISC45 dataset which is the largest dataset of remote sensing scene images. ... Many deep learning based models have proposed to classify images of different dataset. But there is no specific model or approach which is best suited for all dataset. ...

doi:10.5815/ijem.2018.04.02 fatcat:ec7w2dl6qbh2xp4xu35g6mz2ey

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. ... 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 ... Finally, we discuss the promising opportunities for further research. Index Terms-Deep learning, remote sensing image, scene classification. I. ...

arXiv:2005.01094v1 fatcat:qz3at3gyvrbtzkluumalvpqb64

Open Access Multiple Versions

Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. ... 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. ... Zhou and Du [27] proposed a deep learning spectralspatial feature based classification framework that jointly uses dimensionality reduction and deep learning techniques for spectral and spatial feature ...

arXiv:1807.11573v1 fatcat:q6vtrod6nvgtrihjafo25iz3wi

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 ... Whereas we are excited about the potential of deep learning for remote sensing, we are equally nervous about whether this technology can deliver. ... and open problems in deep learning for remote sensing, discusses modifications of DL architectures for remote sensing, provides an overview of deep learning tools, and gives an extensive summary of remote ...

doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. ... Experimental results on two remote sensing datasets demonstrate that the proposed method achieves the state-of-art retrieval and classification performance. ... Index Terms-Deep learning, hashing learning, remote sensing, retrieval, classification. I. ...

arXiv:1909.04614v1 fatcat:5bxzkxwevzbmfabellsaj6jpgy

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. ... Section II reviews several publicly available datasets for remote sensing image scene classification. Section III surveys three categories of approaches in this domain. ...

doi:10.1109/jproc.2017.2675998 fatcat:szqrkysja5ffznxn2fq7vgo6j4

Multiple Versions

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for ... In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used ... Three-dimensional CNN is mostly used for multi-frame image classification in which the temporal dimension is added to the domain (spatio-temporal classification). ...

doi:10.3390/rs12152495 fatcat:2zcqsuejjrcplmj4dycgsyen7m

DOAJ Szczepanski

3-D Deep Learning Approach for Remote Sensing Image Classification

Preserved Fulltext

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

Preserved Fulltext

Region-Wise Deep Feature Representation for Remote Sensing Images

Preserved Fulltext

Compact Deep Color Features for Remote Sensing Scene Classification

Preserved Fulltext

Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

Preserved Fulltext

THE POTENTIAL OF BUILDING DETECTION FROM SAR AND LIDAR USING DEEP LEARNING

Preserved Fulltext

A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA

Preserved Fulltext

Big Data and Machine Learning with Hyperspectral Information in Agriculture

Preserved Fulltext

Remote Sensing Image Scene Classification

Preserved Fulltext

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

Preserved Fulltext

Other Versions

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

Preserved Fulltext

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

Preserved Fulltext

Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images [article]

Preserved Fulltext

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

Preserved Fulltext

Deep Learning for Land Use and Land Cover Classification based on Hyperspectral and Multispectral Earth Observation Data: A Review

Preserved Fulltext