Latest issue of IJRS

Volume 43, Issue 5

The latest issue of the International Journal of Remote Sensing is now available.

This issue includes fifteen research articles across various themes and disciplines within remote sensing.

You can access the issue here.

Summary of published articles:

S. Zhang (Henan University) and colleagues propose a method for detection of aircraft in large scale remote sensing imagery drawing upon convolutional neural networks (CNN) and visual saliency.

https://doi.org/10.1080/01431161.2022.2049915

Y. Shangguan (Zhejiang University) and colleagues use Sentinel-2 satellite imagery, Google Earth Engine and the Random Forest machine learning method to examine spatiotemporal change in soybean planting in Argentina.

https://doi.org/10.1080/01431161.2022.2049913

J. Cheng (Beijing Normal University) and colleagues propose a direct algorithm for the estimation of clear-sky surface longwave net radiation (SLNR) from MODIS imagery.

https://doi.org/10.1080/01431161.2022.2048116

K. Shen and colleagues (Beihang University) introduce a dual-output, cross-scale deep learning-based pansharpening method, DOCSNet, tested with GaoFen-2 and WorldView-3 satellite imagery.

https://doi.org/10.1080/01431161.2022.2042618

S. Zhu (Chinese Academy of Sciences) and colleagues examine the Dunhuang radiometric calibration site and reevaluate its homogeneity using Landsat-8 and Sentinel-2 satellite imagery.

https://doi.org/10.1080/01431161.2022.2048117

Q. Sun (China Agricultural University) and colleagues establish a remote sensing-based method for the assessment of rice lodging grade using Sentinel-2 satellite imagery and change vector analysis (CVA).

https://doi.org/10.1080/01431161.2021.2012293

S. Xiong (Peking University) and colleagues describe a method which generates dense time series images at high (10 m) spatial resolution through fusion of Landsat-7, Landsat-8 and Sentinel-2 surface reflectance imagery.

https://doi.org/10.1080/01431161.2022.2047240

A. Camacho and colleagues (Universidad Industrial de Santander) propose a hyperspectral and multispectral image fusion algorithm which addresses spectral variability with an augmented linear mixing model (ALMM).

https://doi.org/10.1080/01431161.2022.2041762

B.A. Semcheddine and A. Daamouche (University M'hamed Bougara Boumerdes) propose a new technique for the extraction of spatial features from very high resolution (VHR) imagery based on matched filters.

https://doi.org/10.1080/01431161.2022.2048318

X. Fu (East China University of Technology) and colleagues examine the impacts of natural disasters on rice yield in Jiangxi, China, with a method which makes use of Landsat and MODIS satellite imagery alongside DEMs and a Standardized Precipitation Index.

https://doi.org/10.1080/01431161.2022.2049914

L. Bennett and colleagues (University of Alberta) present ITAM-T, an image to attribute model for trees, and use it to identify and classify individual trees in RGB drone imagery for wildfire fuel characterization purposes.

https://doi.org/10.1080/01431161.2022.2048914

REVIEW ARTICLE: Y. Bai and colleagues (Chang'an University) explore the use of deep learning techniques in remote sensing. Over 2,600 papers on deep learning with remote sensing were published between 2014 and 2020 with convolutional neural networks (CNN) most-used.

https://doi.org/10.1080/01431161.2022.2048319

Z. He (Hebei University of Technology) and colleagues introduce a novel, semi-supervised anchor graph ensemble (SAGE) for large-scale hyperspectral image classification.

https://doi.org/10.1080/01431161.2022.2048916

C. Hasan and colleagues (University of Luxembourg) propose a multispectral, edge-filtered generative adversarial network (GAN)-based architecture for removal of clouds from satellite imagery.

https://doi.org/10.1080/01431161.2022.2048915

T.T.N. Trieu (National Institute for Environmental Studies) and colleagues use the Total Carbon Column Observing Network (TCCON), sky radiometer and LiDAR data to examine the influences of aerosols and thin cirrus clouds on GOSAT carbon dioxide and methane observations.

https://doi.org/10.1080/01431161.2022.2038395