385
Views
13
CrossRef citations to date
0
Altmetric
Original Articles

Automatic Pseudo-invariant Feature Extraction for the Relative Radiometric Normalization of Hyperion Hyperspectral Images

, , , &
Pages 755-773 | Published online: 15 May 2013

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (2)

Gaurav Kumar, Akshay Kumar & Rajiv Gupta. (2022) Relative radiometric normalization for mosaicking IRS CartoSat-2 panchromatic images using genetic algorithm. Geocarto International 37:26, pages 11614-11632.
Read now
Arati Paul, Susmit Bhattacharya, Dibyendu Dutta, Jaswant Raj Sharma & Vinay Kumar Dadhwal. (2015) Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms. GIScience & Remote Sensing 52:6, pages 643-659.
Read now

Articles from other publishers (11)

Lei Chen, Ying Ma, Yi Lian, Hu Zhang, Yanmiao Yu & Yanzhen Lin. (2023) Radiometric Normalization Using a Pseudo−Invariant Polygon Features−Based Algorithm with Contemporaneous Sentinel−2A and Landsat−8 OLI Imagery. Applied Sciences 13:4, pages 2525.
Crossref
Hanzeyu Xu, Yuyu Zhou, Yuchun Wei, Chong Liu, Xiao Li & Wei Chen. (2023) A Relative Radiometric Normalization Method for Enhancing Radiometric Consistency of Landsat Time-Series Imageries. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16, pages 5797-5812.
Crossref
Armin Moghimi, Amin Sarmadian, Ali Mohammadzadeh, Turgay Celik, Meisam Amani & Huseyin Kusetogullari. (2022) Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features. IEEE Transactions on Geoscience and Remote Sensing 60, pages 1-20.
Crossref
Taeheon Kim & Youkyung Han. (2021) Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction. Remote Sensing 13:19, pages 3990.
Crossref
Bardia Yousefi, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier P. V. Maldague & Georges Beaudoin. (2021) Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery. Remote Sensing 13:11, pages 2125.
Crossref
Dae Kyo Seo & Yang Dam Eo. (2019) Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery. Applied Sciences 9:21, pages 4543.
Crossref
Arati Paul & Nabendu Chaki. (2019) Dimensionality Reduction of Hyperspectral Images Using Pooling. Pattern Recognition and Image Analysis 29:1, pages 72-78.
Crossref
Lino Garda Denaro, Bo-Yi Lin, Muhammad Aldila Syariz & Lalu Muhamad Jaelani. (2018) Pseudoinvariant feature selection for cross-sensor optical satellite images. Journal of Applied Remote Sensing 12:04, pages 1.
Crossref
Younggi Byun & Dongyeob Han. (2018) Relative radiometric normalization of bitemporal very high-resolution satellite images for flood change detection. Journal of Applied Remote Sensing 12:02, pages 1.
Crossref
Luigi BarazzettiMarco GianinettoMarco Scaioni. Radiometric normalization with multi-image pseudo-invariant features. Radiometric normalization with multi-image pseudo-invariant features.
Luis Garcia-Torres, Juan J. Caballero-Novella, David Gómez-Candón & Ana Isabel De-Castro. (2014) Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features. PLoS ONE 9:3, pages e91275.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.