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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 2491

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Special Issue Editors

Dr. Qi Ming

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Guest Editor

College of Computer Science, Beijing University of Technology, Beijing, China
Interests: generic object detection; oriented object detection; remote sensing; deep learning theory
Special Issues, Collections and Topics in MDPI journals

Dr. Xudong Zhao

E-Mail Website
Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Interests: hyperspectral image processing; multi-source remote sensing classification and fractional signal processing

Special Issue Information

Dear Colleagues,

With the rapid development of remote sensing technology, the availability of remote sensing data has rapidly increased, forming the basis for a wide range of applications in the field of Earth observation, covering a wide range of research areas, including agricultural production, climate monitoring, geological exploration, and more. However, the raw signals received by satellite, airborne, and ground-based sensors are susceptible to various distortions, including radiometric inconsistencies, geometric offsets, and atmospheric or topographic effects. Therefore, such data require careful preprocessing and calibration to ensure their consistency, comparability, and physical interpretability across time, space, and sensor types. This special issue, "Remote Sensing Data Preprocessing and Calibration," invites submissions to advance methods and practices for data correction, normalization, and harmonization. We particularly welcome the application of deep learning methods to enhance preprocessing processes and optimize uncertainty in data calibration field. This special issue aims to strengthen the application foundation of remote sensing data in diverse fields such as environmental resources, agriculture, and climate science by improving its quality and reliability.

Dr. Qi Ming
Dr. Xudong Zhao
Guest Editors

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 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-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 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.

Keywords

remote sensing preprocessing
deep learning
image processing
geometric correction
cross-sensor harmonization
multi-source data fusion
time-series consistency
uncertainty assessment
multi-modality data processing
cloud-based processing

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Published Papers (4 papers)

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Research

23 pages, 6159 KB

Open AccessArticle

GIDNet: Infrared Small Target Detection Network Based on Gradient-Intensity Decoupled

by Xianwei Gao, Jingtao Wu, Dafeng Cao, Haotian Xu, Yingjie Ma, Lu Li and Mingjing Zhao

Remote Sens. 2026, 18(10), 1527; https://doi.org/10.3390/rs18101527 - 12 May 2026

Viewed by 253

Abstract

Infrared small target detection (IRSTD) plays a pivotal role in a comprehensive set of applications. Despite the extensive research alongside numerous algorithms proposed in recent years, IRSTD remains a formidable task, primarily stemming from the inherently low level of signal-to-noise ratios (SNR) as well as the presence of intricate background clutter. Current models remain constrained by three critical bottlenecks: the degradation of spectral coupling between intensity and gradient information in deep layers, limited scale adaptability of static filters, and the loss of spatial precision caused by iterative downsampling. We propose GIDNet, a gradient-intensity decoupled network that balances target energy preservation and noise suppression to address the aforementioned issues. Our GIDNet architecture incorporates three core components: a gradient-intensity synergistic convolution (GISC) designed to synergistically encode intensity and gradient information for robust target enhancement; a multi-scale difference contrast (MSDC) module for scale-adaptive detection via adaptive contrast modeling; and a shallow feature projection (SFP) strategy aimed at maintaining precise spatial localization by bridging the gap between deep semantics and shallow spatial details. Comprehensive evaluations, encompassing both quantitative metrics and qualitative visualizations, consistently demonstrate the preeminence of the developed GIDNet surpassing the performance of 16 counterparts. Full article

(This article belongs to the Special Issue Remote Sensing Data Preprocessing and Calibration)

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30 pages, 11402 KB

Open AccessArticle

Striping Noise Reduction: A Detector-Selection Approach in Multi-Column Scanning Radiometers

by Xiaowei Jia, Xiuju Li, Tao Wen and Changpei Han

Remote Sens. 2026, 18(2), 233; https://doi.org/10.3390/rs18020233 - 11 Jan 2026

Viewed by 637

Abstract

Striping noise is a common problem in multi-detector scanning radiometers on remote sensing satellites, typically caused by response inconsistency among detector elements. For payloads with a multi-column redundant architecture, this paper proposes a detector-selection framework that jointly considers sensitivity and uniformity from the perspective of detector-element selection to mitigate striping noise. First, the degree of detector consistency is quantified using the Inter-Row Brightness Temperature Difference (IRBTD). Then, a dynamic programming approach based on the Viterbi algorithm is employed to select detector elements row by row with linear time complexity, optimizing the process through a weighted cost function that integrates sensitivity and consistency. Experiments on raw data from the FY-4B Geostationary High-speed Imager (GHI) show that the method reduces inconsistency by 10–40% while increasing the noise-equivalent temperature difference (NEdT) by only 1–4% (≤4 mK). The average IRBTD decreases by approximately 20–100 mK, and high-frequency striping energy is significantly suppressed (reduction of 50–90%). The algorithm exhibits linear time complexity and low computational overhead, making it suitable for real-time on-board processing. Its weighting parameter enables flexible trade-offs between sensitivity and uniformity. By suppressing striping noise directly during the detector-selection stage without introducing data distortion or requiring calibration adjustments, the proposed method can be widely applied to scanning radiometers that employ multi-column long-linear-arrays. Full article

(This article belongs to the Special Issue Remote Sensing Data Preprocessing and Calibration)

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23 pages, 4663 KB

Open AccessArticle

Element Evaluation and Selection for Multi-Column Redundant Long-Linear-Array Detectors Using a Modified Z-Score

by Xiaowei Jia, Xiuju Li and Changpei Han

Remote Sens. 2026, 18(2), 224; https://doi.org/10.3390/rs18020224 - 9 Jan 2026

Viewed by 478

Abstract

New-generation geostationary meteorological satellite radiometric imagers widely employ multi-column redundant long-linear-array detectors, for which the Best Detector Selection (BDS) strategy is crucial for enhancing the quality of remote sensing data. Addressing the limitation of current BDS methods that often rely on a single metric and thus fail to fully exploit the detector’s comprehensive performance, this paper proposes a detector evaluation method based on a modified Z-score. This method systematically categorizes detector metrics into three types: positive, negative, and uniformity. It introduces, for the first time, spectral response deviation (SRD) as an effective quantitative measure for the Spectral Response Function (SRF) and employs a robust normalization strategy using the Interquartile Range (IQR) instead of standard deviation, enabling multi-dimensional detector evaluation and selection. Validation using laboratory data from the FY-4C/AGRI long-wave infrared band demonstrates that, compared to traditional single-metric optimization strategies, the best detectors selected by our method show significant improvement across multiple performance indicators, markedly enhancing both data quality and overall system performance. The proposed method features low computational complexity and strong adaptability, supporting on-orbit real-time detector optimization and dynamic updates, thereby providing reliable technical support for high-quality processing of remote sensing data from geostationary meteorological satellites. Full article

(This article belongs to the Special Issue Remote Sensing Data Preprocessing and Calibration)

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24 pages, 5153 KB

Open AccessArticle

Temperature-Field Driven Adaptive Radiometric Calibration for Scan Mirror Thermal Radiation Interference in FY-4B GIIRS

by Xiao Liang, Yaopu Zou, Changpei Han, Pengyu Huang, Libing Li and Yuanshu Zhang

Remote Sens. 2025, 17(24), 3948; https://doi.org/10.3390/rs17243948 - 6 Dec 2025

Viewed by 538

Abstract

To meet the growing demand for quantitative remote sensing applications in GIIRS radiometric calibration, this paper proposes a temperature field-driven adaptive scan mirror thermal radiation interference correction method. Based on the on-orbit deep space observation data from the Fengyun-4B satellite, this paper systematically analyzes the thermal radiation interference characteristics caused by scan mirror deflection and constructs the first scan mirror thermal radiation response model suitable for GIIRS. On the basis of this model, this paper further introduces the dynamic variation characteristics of the internal thermal environment of the instrument, enabling adaptive response and compensation for radiation disturbances. This method overcomes the limitations of relying on static calibration parameters and improves the generality and robustness of the model. Independent validation results show that this method effectively suppresses the interference of scan mirror deflection on instrument background radiation and enhances the consistency of the deep space and blackbody spectral diurnal variation time series. After correction, the average system bias of the interference-sensitive channel decreased by 94%, and the standard deviation of radiance bias from 2.5 mW/m²·sr·cm⁻¹ to below 0.5 mW/m²·sr·cm⁻¹. In the O-B test, the maximum improvement in relative standard deviation reached 0.15 K. Full article

(This article belongs to the Special Issue Remote Sensing Data Preprocessing and Calibration)

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