Special Issue "Pixel-Based Image Compositing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Prof. Ricardo da Silva Torres
E-Mail Website
Guest Editor
Institute of Computing, University of Campinas (UNICAMP)
Tel. +55 19 35215887
Interests: multimedia analysis; multimedia retrieval; machine learning; image representation

Special Issue Information

Dear Colleagues,

Pixel-based image compositing algorithms exploit pixel properties (e.g., location and spectral information) aiming to create spatially-contiguous image composites across large areas. The goal is to create high-quality, noisy-free, and consistent datasets to support a wide range of applications based on remote sensing imagery. Examples of applications, which often benefit from the use of image composites, include land cover mapping, land use modeling, change detection and understanding, ecosystem monitoring, and urban planning.

Despite the huge progress in the area in the past decades with regard to the development of effective algorithms and models for pixel-based image composition, the generation of high-quality images to be used in such applications is still a cumbersome task. Common challenges include: Cloud, haze, and aerosol contamination; proper handling of boundary artifacts; lack of consistency across different bands; inexistence of methods to define suitable composite period lengths; lack of efficient algorithms for dealing with massive datasets.

The overarching goal of this Special Issue is to present state-of-the-art research outcomes in pixel-based image compositing, focusing on both novel effective and efficient algorithms, and existing needs in emerging applications and case studies.

Prof. Ricardo da Silva Torres
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Rule-based and statistical methods
  • Multicriteria pixel selection algorithms
  • Machine learning for pixel-based image compositing
  • Efficient and scalable algorithms
  • Performance evaluation and benchmark datasets
  • Pixel-based image compositing for UAV aerial imagery
  • Emerging applications and case studies

Published Papers (1 paper)

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Research

Open AccessArticle
Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction
Remote Sens. 2019, 11(16), 1925; https://doi.org/10.3390/rs11161925 - 17 Aug 2019
Abstract
Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) [...] Read more.
Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images. Full article
(This article belongs to the Special Issue Pixel-Based Image Compositing)
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