Results 11 to 20 of about 3,972,386 (310)
An Uncertainty Descriptor for Quantitative Measurement of the Uncertainty of Remote Sensing Images
Remote Sensing, 2019 Reliable image classification results are crucial for the application of remote sensing images, but the reliability of image classification has received less attention. In particular, the inherent uncertainty of remote sensing images has been disregarded.
Qi Zhang, Penglin Zhang
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A Novel Deep Nearest Neighbor Neural Network for Few-Shot Remote Sensing Image Scene Classification
Remote Sensing, 2023 Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images.
Yanqiao Chen +4 more
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A Review of Remote Sensing Image Dehazing [PDF]
Sensors, 2021 Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently,
Juping Liu +4 more
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Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
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Spatiotemporal Image Fusion in Remote Sensing [PDF]
Remote Sensing, 2019 In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on
Mariana Belgiu, Alfred Stein
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Multiresolution Remote Sensing Image Clustering [PDF]
IEEE Geoscience and Remote Sensing Letters, 2009 With the multiplication of satellite images with complementary spatial and spectral resolution, a major issue in the classification process is the simultaneous use of several images. In this context, the objective of this letter is to propose a new method which uses information contained in both spatial resolutions.
Wemmert, Cédric +3 more
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Object-Based Land Cover Classification for ALOS Image Combining TM Spectral [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 Land cover classification for high spatial resolution remote sensing images becomes a challenging work. The high spatial resolution remote sensing images have more spatial information.
G. Wang, J. Liu, G. He
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Atmospheric Light Estimation Based Remote Sensing Image Dehazing
Remote Sensing, 2021 Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such ...
Zhiqin Zhu +7 more
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Multiscale Classification of Remote Sensing Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2012 A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task.
Jefersson Alex dos Santos +4 more
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REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 Abstract. Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-scale standard datasets to
M. Schmitt, M. Schmitt, Y.-L. Wu
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