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Earth Observation Using Satellite Global Images of Remote Sensing (2nd Edition)

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Dear Colleagues,

The recent growing availability of satellite remote sensing imagery is becoming increasingly important as more and more satellite imagery is made available from various sensors, even for free, and new groups of satellites are put into orbit to allow global analyses. The same applications are continuously expanding: today they enable land cover and land use mapping (specifically, detection of urbanization, also at global level, monitoring of cultivated land in order to detect water stress and desertification, and in order to direct crop operations); they also are employed for detecting and monitoring air, land and sea pollution, and for fighting forest wildfires.

The Previous Special Issue “Earth Observation Using Satellite Global Images of Remote Sensing” was a great success. We are pleased to invite you to contribute to new Special Issue in subjects related to the journal scope. This Special Issue also aims to explore the recent progresses of in the field of satellite Remote Sensing and possible further developments.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Image analysis applications to remote sensing;
Image segmentation;
Image classification;
Image edge detection;
Change detection;
Image processing and pattern recognition;
Mathematical morphology;
Object Based Image Analysis;
Feature extraction;
Remote sensing applications;
Microwave Remote Sensing;
SAR.

We look forward to receiving your contributions.

Dr. Giuliana Bilotta
Prof. Dr. Jon Atli Benediktsson
Guest Editors

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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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 2700 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.

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18 pages, 5133 KB

Open AccessTechnical Note

Suppressing the Patch-like Errors of SAR Intensity Offset Tracking Based on Z-Score Standardization and INFLO Structural Density Analysis

by Lingshuai Kong, Jia Li, Xuyan Ma, Zhenqi Song, Long Li, Jiahao Dian, Huiguo Ye, Xunzhe Dai and Jiaqiao Li

Remote Sens. 2026, 18(10), 1528; https://doi.org/10.3390/rs18101528 - 12 May 2026

Viewed by 502

Abstract

Offset tracking based on normalized cross-correlation (NCC) of synthetic aperture radar (SAR) intensity imagery serves as a critical technique for monitoring large-scale ground deformations. However, traditional NCC of SAR intensity imagery is susceptible to isolated high-intensity points, which can induce patch-like errors and compromise the reliability of the derived deformation fields. Existing suppression methods do not differentiate between isolated high-intensity points and those constituting structural features, which are beneficial for NCC, resulting in a substantial loss of valid offset measurements concurrent with errors mitigation. Regarding this, we proposed a method for suppressing patch-like errors of SAR intensity offset tracking. The new method initially employs Z-score standardization to rapidly screen high-intensity points; subsequently, Influenced Outlierness (INFLO) structural density analysis is utilized to identify isolated high-intensity points (classified as outliers), which are then replaced by the median values of their local neighborhood prior to the NCC computation. A method for detecting patch-like errors was also designed based on the spatial characteristics of patch-like errors, defined by abrupt boundary discontinuities and high internal homogeneity. On this basis, quantitative metrics including the patch-like errors removal rate and the valid offset coverage rate were further designed to evaluate the approach’s capability in eliminating patch-like errors while retaining valid offset measurements. Comparative experiments were conducted using simulated and real SAR data. Results demonstrate that the proposed method achieves patch-like errors suppression comparable to existing methods while significantly enhancing the retention of valid offset measurements and improving overall estimation accuracy. Specifically, in the real data experiments over the Amnye Machen and Central Tianshan test areas, compared to the logarithmic weighted NCC, the proposed method increased the valid offset coverage rates by 0.272 and 0.264, and improved the comprehensive quality indices by 0.191 and 0.184, respectively. This study represents a refinement of classical deformation estimation methodologies, offering a more robust option for monitoring large-scale ground deformation. Full article

(This article belongs to the Special Issue Earth Observation Using Satellite Global Images of Remote Sensing (2nd Edition))

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