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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: closed (16 June 2023) | Viewed by 16413

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Special Issue Editors

Prof. Dr. Riccardo Roncella

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Guest Editor

Department of Engineering and Architecture, Università degli Studi di Parma, Parco Area delle Scienze, 181/a, 43124 Parma, PR, Italy
Interests: image matching; image orientation; satellite/airborne/UAV photogrammetry; 3D reconstruction; monitoring; laser scanning; vision metrology
Special Issues, Collections and Topics in MDPI journals

Dr. Ricardo Torres

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Guest Editor

1. Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, NTNU – Norwegian University of Science and Technology, Ålesund, Norway
2. Farm Technology Group and Wageningen Data Competence Center, Wageningen University and Research, Wageningen, The Netherlands
Interests: multimedia analysis; multimedia retrieval; machine learning; information visualization; databases

Special Issue Information

Dear Colleagues,

As remote sensing technologies and methods continue to improve in recent decades, scientists have made great strides in the field of remote sensing image processing. Satellite, airborne, UAV, and terrestrial imaging techniques are constantly evolving in terms of data volume, quality, and variety. Automated processing methods are needed to readily derive information relevant to users and stakeholders, and the development, deployment and dissemination of new approaches are strategic for a wide range of applications as well as for identifying where their use could achieve their full potential. The ability to collect multiple data sources has led to an impressive growth of applications, which in some cases are already integrated into the routine procedures of institutions that focus on land monitoring. Experimental research developed in recent years suggests that consistent and accurate results can be expected from such systems, with important implications for the feasibility of new projects and their costs. For example, they can provide the basis for timely and efficient analysis in a variety of fields, such as land use and environmental monitoring, cultural heritage, archaeology, precision agriculture, human activity monitoring, and other fields of practical interest and involved research.

The Special Issue will be a collection of articles focusing on new insights, new developments, current challenges and future prospects in the field of remote sensing image processing. It aims to present the latest advances in innovative image analysis and processing techniques and their contribution in a wide range of application areas, in an effort to predict where they will take the discipline and practice in the coming years.

Dr. Riccardo Roncella
Dr. Ricardo Torres
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 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 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

deep learning
image classification
3D reconstruction
image matching
image registration
object-based image analysis
change detection
data/sensor fusion
pattern recognition

Published Papers (7 papers)

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Research

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28 pages, 685 KiB

Open AccessArticle

A Hybrid Binary Dragonfly Algorithm with an Adaptive Directed Differential Operator for Feature Selection

by Yilin Chen, Bo Gao, Tao Lu, Hui Li, Yiqi Wu, Dejun Zhang and Xiangyun Liao

Remote Sens. 2023, 15(16), 3980; https://doi.org/10.3390/rs15163980 - 11 Aug 2023

Cited by 1 | Viewed by 939

Abstract

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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27 pages, 6147 KiB

Open AccessArticle

Experimental Tests and Simulations on Correction Models for the Rolling Shutter Effect in UAV Photogrammetry

by Nazarena Bruno and Gianfranco Forlani

Remote Sens. 2023, 15(9), 2391; https://doi.org/10.3390/rs15092391 - 03 May 2023

Cited by 2 | Viewed by 1357

Abstract

Many unmanned aerial vehicles (UAV) host rolling shutter (RS) cameras, i.e., cameras where image rows are exposed at slightly different times. As the camera moves in the meantime, this causes inconsistencies in homologous ray intersections in the bundle adjustment, so correction models have been proposed to deal with the problem. This paper presents a series of test flights and simulations performed with different UAV platforms at varying speeds over terrain of various morphologies with the objective of investigating and possibly optimising how RS correction models perform under different conditions, in particular as far as block control is concerned. To this aim, three RS correction models have been applied in various combinations, decreasing the number of fixed ground control points (GCP) or exploiting GNSS-determined camera stations. From the experimental tests as well as from the simulations, four conclusions can be drawn: (a) RS affects primarily horizontal coordinates and varies notably from platform to platform; (b) if the ground control is dense enough, all correction models lead practically to the same mean error on checkpoints; however, some models may cause large errors in elevation if too few GCP are used; (c) in most cases, a specific correction model is not necessary since the affine deformation caused by RS can be adequately modelled by just applying the extended Fraser camera calibration model; (d) using GNSS-assisted block orientation, the number of necessary GCP is strongly reduced. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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17 pages, 9291 KiB

Open AccessEditor’s ChoiceArticle

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

by Alejandro Román, Antonio Tovar-Sánchez, Adam Gauci, Alan Deidun, Isabel Caballero, Emanuele Colica, Sebastiano D’Amico and Gabriel Navarro

Remote Sens. 2023, 15(1), 237; https://doi.org/10.3390/rs15010237 - 31 Dec 2022

Cited by 9 | Viewed by 4678

Abstract

Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m³ and 10 to 20 mg/m³, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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25 pages, 8841 KiB

Open AccessArticle

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

by Zhengbin Wang, Anxi Yu, Ben Zhang, Zhen Dong and Xing Chen

Remote Sens. 2022, 14(19), 5060; https://doi.org/10.3390/rs14195060 - 10 Oct 2022

Cited by 2 | Viewed by 1769

Abstract

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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22 pages, 8813 KiB

Open AccessArticle

Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification

by Andrzej Stateczny, Shanthi Mandekolu Bolugallu, Parameshachari Bidare Divakarachari, Kavithaa Ganesan and Jamuna Rani Muthu

Remote Sens. 2022, 14(19), 4837; https://doi.org/10.3390/rs14194837 - 28 Sep 2022

Cited by 8 | Viewed by 1512

Abstract

Land Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various spectral feature bands are involved, but unexpectedly little consideration has been given to these characteristics in deep learning models. Due to the wide availability of RGB models in computer vision, this research mainly utilized RGB bands. Once the pre-processing is carried out for the images of the selected dataset, the hybrid feature extraction is performed using Haralick texture features, an oriented gradient histogram, a local Gabor binary pattern histogram sequence, and Harris Corner Detection to extract features from the images. After that, the Improved Mayfly Optimization (IMO) method is used to choose the optimal features. IMO-based feature selection algorithms have several advantages that include features such as a high learning rate and computational efficiency. After obtaining the optimal feature selection, the LULC classes are classified using a multi-class classifier known as the Multiplicative Long Short-Term Memory (mLSTM) network. The main functionality of the multiplicative LSTM classifier is to recall appropriate information for a comprehensive duration. In order to accomplish an improved result in LULC classification, a higher amount of remote sensing data should be processed. So, the simulation outcomes demonstrated that the proposed IMO-mLSTM efficiently classifies the LULC classes in terms of classification accuracy, recall, and precision. When compared with ConvNet and Alexnet, the proposed IMO-mLSTM method accomplished accuracies of 99.99% on Sat 4, 99.98% on Sat 6, and 98.52% on the Eurosat datasets. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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Review

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29 pages, 9992 KiB

Open AccessReview

Weakly Supervised Object Detection for Remote Sensing Images: A Survey

by Corrado Fasana, Samuele Pasini, Federico Milani and Piero Fraternali

Remote Sens. 2022, 14(21), 5362; https://doi.org/10.3390/rs14215362 - 26 Oct 2022

Cited by 5 | Viewed by 2637

Abstract

The rapid development of remote sensing technologies and the availability of many satellite and aerial sensors have boosted the collection of large volumes of high-resolution images, promoting progress in a wide range of applications. As a consequence, Object detection (OD) in aerial images has gained much interest in the last few years. However, the development of object detectors requires a massive amount of carefully labeled data. Since annotating datasets is very time-consuming and may require expert knowledge, a consistent number of weakly supervised object localization (WSOL) and detection (WSOD) methods have been developed. These approaches exploit only coarse-grained metadata, typically whole image labels, to train object detectors. However, many challenges remain open due to the missing location information in the training process of WSOD approaches and to the complexity of remote sensing images. Furthermore, methods studied for natural images may not be directly applicable to remote sensing images (RSI) and may require carefully designed adaptations. This work provides a comprehensive survey of the recent achievements of remote sensing weakly supervised object detection (RSWSOD). An analysis of the challenges related to RSWSOD is presented, the advanced techniques developed to improve WSOD are summarized, the available benchmarking datasets are described and a discussion of future directions of RSWSOD research is provided. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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Other

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13 pages, 3539 KiB

Open AccessTechnical Note

Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

by Sihan Yang, Fei Song, Gwanggil Jeon and Rui Sun

Remote Sens. 2022, 14(15), 3709; https://doi.org/10.3390/rs14153709 - 03 Aug 2022

Cited by 6 | Viewed by 1601

Abstract

High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensing which includes scene classification and change detection. To enhance the feature representation of images in scene classification, a robust label semantic relation learning (LSRL) network based on EfficientNet is presented for scene classification. It consists of a semantic relation learning module based on graph convolutional networks and a joint expression learning framework based on similarity. Since the bi-temporal remote sensing image pairs include spectral information in both temporal and spatial dimensions, land cover and land use change monitoring can be improved by using the relationship between different spatial and temporal locations. Therefore, a change detection method based on swin transformer blocks (STB-CD) is presented to obtain contextual relationships between targets. The experimental results on the LEVIR-CD, NWPU-RESISC45, and AID datasets demonstrate the superiority of LSRL and STB-CD over other state-of-the-art methods. Full article

(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)

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