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Remote Sensing Image Visualization Using Double Layers
2006
2006 IEEE International Symposium on Geoscience and Remote Sensing
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Thus in remote sensing image processing we deal with mixed pixels instead of pure pixels as in traditional digital image processing. ...
Remote sensing imagery has relatively low spatial resolution. ...
In this paper we present a new approach that uses double layers to visualize remote sensing image. It employs two layers to visualize the mixed pixel information with different levels of details. ...
doi:10.1109/igarss.2006.68
dblp:conf/igarss/CaiDM06
fatcat:f7v5acqm6fg73jpxdm2zl7pv3a
SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES
2016
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Super-resolution reconstruction of sequence remote sensing image is a technology which handles multiple low-resolution satellite remote sensing images with complementary information and obtains one or ...
First, the sequence images were decomposed into a detail layer containing the detail information and a smooth layer containing the large scale edge information by bilateral filter. ...
With the development of space information technology, such as the domestic remote sensing satellite, navigation and positioning, satellite remote sensing image have been widely used in various fields and ...
doi:10.5194/isprs-annals-iii-7-213-2016
fatcat:lcs643iswngyfo6wjiaskywmxe
Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers
2018
Water
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Cloud and snow detection is one of the most significant tasks for remote sensing image processing. ...
However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. ...
Accurate detection of clouds and snow for remote sensing images is a key task for many remote sensing applications [3] . ...
doi:10.3390/w10111666
fatcat:2ykuoxpjszejrlka6lk2mbp5c4
RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
2021
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Based on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images ...
Just skip the trial process vector data update and cumbersome process of mapping the basic map elements can be automatically transform, the image on the main streets and typical rules of construction material ...
Figure 8 . 8 Remote sensing image conversion network map. ...
doi:10.5194/isprs-archives-xliii-b3-2021-285-2021
fatcat:5wo3je6s7vej3li5fzbsdb5gau
Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method
2021
Remote Sensing
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Objects in remote sensing images are different from those in natural images. ...
Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. ...
Remote Sens. 2021, 13, 4517 ...
doi:10.3390/rs13224517
fatcat:ns6ecfwhqvbivdhwj77pggnzgm
Hyperspectral Imagery Visualization Using Double Layers
2007
IEEE Transactions on Geoscience and Remote Sensing
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In our research, we are particularly interested in the display of mixed-pixel classification results, since most pixels in a remotely sensed hyperspectral image are mixed pixels. ...
A visualization approach that uses classification as an intermediate step may maximize the information transfer. ...
CONCLUSION This paper presents a novel approach to visualize hyperspectral remote sensing images by employing double color layers. ...
doi:10.1109/tgrs.2007.894922
fatcat:fbudsjgs5bb2ziel4yhfokav4m
Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images
2020
IEEE Access
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Noting the shortcomings of current methods in detecting small objects in image-based remote sensing applications, in this paper, we propose a novel implementation of single shot multibox detector (SSD) ...
In the data processing step of the model, we use the image segmentation of the feature point region proposals to improve the training sample size. ...
Finally, we use double segmentation on the remote sensing image with feature points to get segmented images of different regions, count the number of feature points in each small image, keep pictures with ...
doi:10.1109/access.2020.2991439
fatcat:5w7dpzko7rhhhgrdreanaqnyj4
Feature-Driven Multilayer Visualization for Remotely Sensed Hyperspectral Imagery
2010
IEEE Transactions on Geoscience and Remote Sensing
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Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. ...
Index Terms-Hyperspectral image visualization, mixed-pixel visualization, multilayer visualization. ...
Our goal is to visualize information and to enhance data features as much as possible. Visualization has been part of remote sensing for decades, beginning with false-color display. ...
doi:10.1109/tgrs.2010.2047021
fatcat:mdtz4e34pjf3blmhsyqidue7oe
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
[article]
2020
arXiv
pre-print
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Change detection is a basic task of remote sensing image processing. ...
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. ...
Overview Compared with general optical remote sensing images, high-resolution remote sensing images have more abundant information and higher requirements for feature extraction. ...
arXiv:2003.03608v1
fatcat:capfvnhgjzha5pspwulrmwxfha
Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery
2017
Remote Sensing
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We believe deformable ConvNets will have excellent performance when used in VHR remote sensing images. ...
Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. ...
Development of very high resolution remote sensing images provide us more detailed geo-spatial objects information, including diversities in scale, orientation and shape. ...
doi:10.3390/rs9121312
fatcat:jxiedtzpwjfohngg4xavdjpqn4
Multilabel Image Annotation Based on Double-Layer PLSA Model
2014
The Scientific World Journal
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The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. ...
By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. ...
Akcay and Aksoy also adopt PLSA model [13] to detect the remote sensing image. ...
doi:10.1155/2014/494387
pmid:24999490
pmcid:PMC4066723
fatcat:3b2cryf3n5ewbhe42bip3ejqhu
SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN
2022
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and ...
Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of ...
It can be found from visual perception that the improved SRGAN could effectively reconstruct spatial resolution of remote sensing images.We will continue to analyze and evaluate the accuracy of the survey ...
doi:10.5194/isprs-annals-v-3-2022-603-2022
fatcat:rzaubejdx5hf7k3nhkybwcwccm
Remote Sensing of Time-Varying Tidal Flat Topography, Jiangsu Coast, China, Based on the Waterline Method and an Artificial Neural Network Model
2020
Applied Sciences
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Then, this ANN model was used to simulate synchronous DEMs corresponding to remote sensing images on 11 February 2012, and 11 July 2013, under low tide conditions. ...
The "7-15-15-1" double hidden layers with optimized training structures were confirmed via continuous training and comparisons. ...
W.L. is responsible for image processing and document correction. J.H. implemented accuracy assessment of the ANN model. X.D. constructed five tide stations and provide tidal level data for images. ...
doi:10.3390/app10103645
fatcat:2hptmp6v3vfcveukhup3g62sgq
A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. ...
It can make full use of the spatial consistency to fuse the base layers and the detail layers. ...
Block scheme of the proposed TsF approach.
1 ) 1 Double-scale feature decomposition: In order to make full use of the features complementarity in different layers, the input feature pair [Y i , Y j ] ...
doi:10.1109/jstars.2020.3041868
fatcat:yz6ydhh7brh3xm2latzudofn7a
Detection of Ocean Internal Waves Based on Modified Deep Convolutional Generative Adversarial Network and WaveNet in Moderate Resolution Imaging Spectroradiometer Images
2023
Applied Sciences
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We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. ...
By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated ...
similar to real remote sensing images. ...
doi:10.3390/app132011235
fatcat:5sqg74tgjjfnpo7lc2pywetnsi
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