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High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective

2017 Remote Sensing  
All the CNN models are trained from scratch using a large-scale and high-resolution remote sensing image archive, which will be published and made available to the public.  ...  Then, a Principal Component Analysis (PCA) is introduced to compress the high-dimensional remote sensing image feature codes learned by traditional CNNs.  ...  In this paper, we investigate and evaluate the performance of remote sensing image retrieval from a dimensional perspective.  ... 
doi:10.3390/rs9070725 fatcat:qacnq6rizzai7ivhek5wgt5s7a

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.  ...  Scene-based feature representation has been acknowledged to be a more effective way to interpret high-resolution remote sensing images [5, 6] which have a spatial resolution of 1.5 m to 4 m, and usually  ... 
doi:10.3390/app9102110 fatcat:oj3acgbmwnhzppxvvjbsn5cfzq

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.  ...  PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class.  ...  ., (2017) investigated how to extract powerful feature representations based on the pre-trained CNNs for high-resolution remote sensing imagery retrieval.  ... 
doi:10.1016/j.isprsjprs.2018.01.004 fatcat:v5oei4amy5a4nbqklertcg74lm

Convolutional Neural Network to Retrieve Water Depth in Marine Shallow Water Area from Remote Sensing Images

Bo Ai, Zhen Wen, Zhenhua Wang, Ruifu Wang, Dianpeng Su, Chengming Li, Fanlin Yang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This article proposes a model based on CNN, which uses different remote sensing images in four spectral bands, red, green, blue, and near-infrared, to retrieve the water depth.  ...  Finally, the model is shown to be highly portable and capable of retrieving water depth data with resolution equal to the spatial resolution of the remote sensing image using only a small amount of input  ...  Using models to retrieve water depth from high-resolution remote-sensing images has become a possibility with the development of remote sensing technology, and much exploratory work on remote sensing image-based  ... 
doi:10.1109/jstars.2020.2993731 fatcat:a7xglko53ffjrfqagirhlytuj4

Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions

Olfa Ben-Ahmed, Thierry Urruty, Noel Richard, Christine Fernandez-Maloigne
2019 Remote Sensing  
With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI)produced by different types of imaging sensors, analyzing and retrieving these images requireeffective image description  ...  Compared to remote sensing RGB images,HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowingprofile materials and organisms that only hyperspectral sensors  ...  [16] proposed two deep feature extraction schemes for high-resolution remote sensing image retrieval.  ... 
doi:10.3390/rs11050600 fatcat:eqwr3w7e4rcdncqsvuod6viw3m

A Modular System Based on U-Net for Automatic Building Extraction from very high-resolution satellite images

Smail Ait El Asri, Samir El Adib, Ismail Negabi, Naoufal Raissouni, K. Slimani, O. Gerasymov, M. Ait Kbir, S. Bennani Dosse, S. Bourekkadi, A. Amrani
2022 E3S Web of Conferences  
In this paper, we propose a CNN-based system capable of efficiently extracting buildings from very high-resolution satellite images, by combining the performances of the two architectures; U-Net and VGG19  ...  However, the use of convolutional neural networks has not been widely used for remote sensing applications until recently.  ...  Recently, high-resolution satellite imagery was providing a new source of data for remote sensing applications.  ... 
doi:10.1051/e3sconf/202235101071 fatcat:zicxyvm5s5eypagbcvcayw53py

Deep Learning Techniques for Geospatial Data Analysis [chapter]

Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam
2020 Learning and Analytics in Intelligent Systems  
(ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques  ...  The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data.  ...  The work presented in this chapter is based on the course material developed to train engineering teachers on the topics of Geospatial Analysis and Product Design Engineering.  ... 
doi:10.1007/978-3-030-49724-8_3 fatcat:yv6stldjcjbclbfcx3d6i3s2um

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
2017 IEEE Geoscience and Remote Sensing Magazine  
Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community.  ...  In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously  ...  IMAGE RETRIEVAL Remote-sensing image retrieval aims at retrieving images having a similar visual content with respect to a query image from a database [109] .  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Centerline Extraction From High-Resolution Remote Sensing Images.  ...  ., +, JSTARS 2020 3503-3520 Toward Remote Sensing Image Retrieval Under a Deep Image Captioning Perspective.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass

Mingxia Yang, Xiaolu Zhou, Zelin Liu, Peng Li, Jiayi Tang, Binggeng Xie, Changhui Peng
2022 Forests  
This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly  ...  Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics.  ...  Urban Tree Detection Based on Street View Images Street view images provide a new opportunity for fine-scale urban ecology research from a ground-level perspective [116] .  ... 
doi:10.3390/f13040616 doaj:0a68188e173247eea1b08733079ae359 fatcat:jjtkqd6affckzmgle4zwfuosem

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

Hui Lu, Qi Liu, Xiaodong Liu, Yonghong Zhang
2021 Journal of Organizational and End User Computing  
This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic.  ...  With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention.  ...  on the structural features of high-resolution remote sensing image space, scene space and se-mantic space based on deep learning, and realized the understanding and description of high-resolution remote  ... 
doi:10.4018/joeuc.20211101.oa6 fatcat:mxuhgviitff3nldf2gzrfiog7e

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

2021 Journal of Organizational and End User Computing  
This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic.  ...  With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention.  ...  on the structural features of high-resolution remote sensing image space, scene space and se-mantic space based on deep learning, and realized the understanding and description of high-resolution remote  ... 
doi:10.4018/joeuc.20211101oa06 fatcat:qrxy6plef5f7fkgvfk6f5sbkwu

A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval

Wei Xiong, Yafei Lv, Yaqi Cui, Xiaohan Zhang, Xiangqi Gu
2019 Remote Sensing  
Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR).  ...  Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes.  ...  PatternNet comprises 38 categories of high-resolution remote sensing images with a size of 256 × 256 pixels selected from Google Earth.  ... 
doi:10.3390/rs11030281 fatcat:4glhbdsc4jcjnof7edmobjixbi

Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization

Jian Kang, Ruben Fernandez-Beltran, Zhen Ye, Xiaohua Tong, Pedram Ghamisi, Antonio Plaza
2020 IEEE Transactions on Geoscience and Remote Sensing  
perspectives, including: 1) classification; 2) clustering; and 3) image retrieval.  ...  With the development of convolutional neural networks (CNN), the semantic understanding of remote sensing scenes has been significantly improved based on their prominent feature encoding capabilities.  ...  Deep learning, metric learning, remote sensing scene characterization, dimensionality reduction. I.  ... 
doi:10.1109/tgrs.2020.2991657 fatcat:pwylrafekfh6dodp6h3jk2kr2q

Deep learning in remote sensing applications: A meta-analysis and review

Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, Brian Alan Johnson
2019 ISPRS journal of photogrammetry and remote sensing (Print)  
A B S T R A C T Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years.  ...  Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of  ...  a high-resolution MS image.  ... 
doi:10.1016/j.isprsjprs.2019.04.015 fatcat:wheurssuyrdetfbzt3qex7lsba
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