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Remote Sensing Letters is a peer-reviewed journal published by Taylor & Francis, covering Remote-Sensing Image Classification, Remote Sensing in Agriculture, Remote Sensing and LiDAR Applications. Over its lifetime it has published 1,872 papers, which have received 30,641 citations.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Remote Sensing Letters 12 (3): 45–58.
Formats any DOI in Remote Sensing Letters style. No sign-up. |
| Publishes research in | Remote-Sensing Image Classification Remote Sensing in Agriculture Remote Sensing and LiDAR Applications Synthetic Aperture Radar (SAR) Applications and Techniques Remote Sensing and Land Use |
| ISSN | 2150-704X |
| Citation impact (2-yr) | 1.48 |
| h-index | 70 |
| i10-index | 746 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The tasselled cap transformation (TCT) is a useful tool for compressing spectral data into a few bands associated with physical scene characteristics with minimal information loss. TCT was originally evolved from the Landsat multi-spectral scanner (MSS) launched in 1972 and is widely adapted to modern sensors. In this study, we derived the TCT coefficients for…
In this letter, a novel deep learning framework for hyperspectral image classification using both spectral and spatial features is presented. The framework is a hybrid of principal component analysis, deep convolutional neural networks (DCNNs) and logistic regression (LR). The DCNNs for hierarchically extract deep features is introduced into hyperspectral image classification for the first time.…
The advantages of object-based classification over the traditional pixel-based approach are well documented. However, the potential limitations of object-based classification remain less explored. In this letter, we assess the advantages and limitations of an object-based approach to remote sensing image classification relative to a pixel-based approach. We first quantified the negative impacts of under-segmentation errors…
The first global night-time light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band carried by the Suomi National Polar-orbiting Partnership (NPP) satellite were released recently. So far, few studies have been conducted to assess the ability of NPP-VIIRS night-time light composite data to extract built-up urban areas. This letter aims to…
Remote sensing images are useful for monitoring the spatial distribution and growth of urban built-up areas because they can provide timely and synoptic views of urban land cover. Although the normalized difference built-up index (NDBI) is useful to map urban built-up areas, it still has some limitations. This study sought to improve the NDBI by…
