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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 (31 January 2021) | Viewed by 39966

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Dr. Costas Panagiotakis

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Associate Professor, Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, 72100 Crete, Greece
Interests: signal processing; image and video analysis; multimedia and pattern recognition

Dr. Eleni Kokinou

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Associate Professor, Department of Agriculture, Hellenic Mediterranean University, Heraklion, 71410 Crete, Greece
Interests: geophysics; geological and environmental studies; data processing and interpretation; geo-modelling and geo-signal processing; geomorphology

Dr. Karantzalos Konstantinos

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Remote Sensing Laboratory, National Technical University of Athens, 15780 Athens, Greece
Interests: hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision; crop type classification; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade access to earth observation data from aerial and satellite platforms have been significantly leveraged allowing the unprecedented monitoring of land and marine environments. Remote Sensing data can be multidimensional signals, multispectral, hyperspectral images, radar data, time series and video sequences. Efficient data analytics in these signals is crucial in order to exploit all historical archives as well as the newly acquired observations, maybe also in near real-time. Fusion with GNSS signals, proximate remote sensing observations is also currently challenging. The applications are vast including numerous environmental monitoring tasks, agriculture, safety, security, engineering, etc fields. This Special Issues focuses on Signal and Image Processing for Remote Sensing and willing to explore and highlight the most recent cutting-edge data fusion and analytics in remote sensing.

In particular, several challenges and open problems still waiting for efficient solutions and novel methodologies via signal and image processing techniques. The main goal of this special issue is to address advanced topics related to signal processing, image processing and analysis, pattern recognition and machine learning for remote sensing.

We would like to invite you to submit research and review articles related to your research with respect to the following topics:

Analysis of multispectral and hyperspectral data
Analysis of SAR and LIDAR signals
Analysis of hydroacoustic, seismic and microwave signals
Analysis of meteorological and GNSS data
Data fusion techniques
Classification of remote sensing data
Pattern recognition for remote sensing
Image segmentation, enhancement and restoration
Object detection and recognition
Machine learning and deep learning
Filtering and Multiresolution Processing
Change detection and analysis of time series
Extraction of geometric and semantic information from SAR
Satellite, airborne, UAV and proximate remote sensing
Remote sensing applications

Dr. Costas Panagiotakis
Dr. Eleni Kokinou
Dr. Konstantinos Karantzalos
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

Remote Sensing
Signal and Image Processing
Image segmentation
Machine learning
Pattern recognition

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Published Papers (12 papers)

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18 pages, 2813 KiB

Open AccessArticle

A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

by Zhenyu Zhuo, Yu Zhou, Lan Du, Ke Ren and Yi Li

Remote Sens. 2021, 13(9), 1820; https://doi.org/10.3390/rs13091820 - 7 May 2021

Cited by 6 | Viewed by 1876

Abstract

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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16 pages, 6970 KiB

Open AccessArticle

Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning

by Maria Papadomanolaki, Stergios Christodoulidis, Konstantinos Karantzalos and Maria Vakalopoulou

Remote Sens. 2021, 13(7), 1294; https://doi.org/10.3390/rs13071294 - 29 Mar 2021

Cited by 16 | Viewed by 3290

Abstract

Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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17 pages, 11953 KiB

Open AccessFeature PaperArticle

Using Geophysics to Characterize a Prehistoric Burial Mound in Romania

by Alexandru Hegyi, Dragoș Diaconescu, Petru Urdea, Apostolos Sarris, Michał Pisz and Alexandru Onaca

Remote Sens. 2021, 13(5), 842; https://doi.org/10.3390/rs13050842 - 24 Feb 2021

Cited by 7 | Viewed by 3225

Abstract

A geophysical investigation was carried across the M3 burial mound from Silvașu de Jos —Dealu Țapului, a tumuli necropolis in western Romania, where the presence of the Yamnaya people was certified archaeologically. For characterizing the inner structure of the mound, two conventional geophysical methods have been used: a geomagnetic survey and electrical resistivity tomography (ERT). The results allowed the mapping of the central features of the mound and the establishment of the relative stratigraphy of the mantle, which indicated at least two chronological phases. Archaeological excavations performed in the central part of the mound accurately validated the non-invasive geophysical survey and offered a valuable chronological record of the long-forgotten archaeological monument. Geophysical approaches proved to be an invaluable instrument for the exploration of the monument and suggest a fast constructive tool for the investigation of the entire necropolis which currently has a number of distinct mounds. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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22 pages, 872 KiB

Open AccessArticle

Hyperspectral Image Enhancement by Two Dimensional Quaternion Valued Singular Spectrum Analysis for Object Recognition

by Yuxin Lin, Bingo Wing-Kuen Ling, Lingyue Hu, Yiting Zheng, Nuo Xu, Xueling Zhou and Xinpeng Wang

Remote Sens. 2021, 13(3), 405; https://doi.org/10.3390/rs13030405 - 25 Jan 2021

Cited by 17 | Viewed by 2347

Abstract

This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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23 pages, 4467 KiB

Open AccessArticle

Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

by Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis and Evangelos Maltezos

Remote Sens. 2021, 13(3), 371; https://doi.org/10.3390/rs13030371 - 21 Jan 2021

Cited by 84 | Viewed by 5729

Abstract

In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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21 pages, 4431 KiB

Open AccessArticle

Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank Decomposition with Texture Feature Extraction

by Shangzhen Song, Yixin Yang, Huixin Zhou and Jonathan Cheung-Wai Chan

Remote Sens. 2020, 12(23), 3966; https://doi.org/10.3390/rs12233966 - 4 Dec 2020

Cited by 4 | Viewed by 2698

Abstract

The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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19 pages, 11581 KiB

Open AccessArticle

A Modified Cartesian Factorized Backprojection Algorithm Integrating with Non-Start-Stop Model for High Resolution SAR Imaging

by Da Liang, Heng Zhang, Tingzhu Fang, Haoyu Lin, Dacheng Liu and Xiaoxue Jia

Remote Sens. 2020, 12(22), 3807; https://doi.org/10.3390/rs12223807 - 20 Nov 2020

Cited by 5 | Viewed by 2362

Abstract

High resolution synthetic aperture radar (SAR) imaging has extensive application value especially in military reconnaissance and disaster monitoring. The motion of the satellite during the transmission and reception of the signal introduces notable errors in the high resolution SAR spotlight mode, which will lead to a defocused SAR image if not handled. To address this problem, an accurate correct echo model based on non-start-stop model is derived to describe the property of the SAR signal in the paper. Then, in the imaging processing, an azimuth-time-varying range frequency modulation rate is used for range compression. The range history and compensation phase are also derived based on the correct echo model. Then, combining the correct echo model and Cartesian factorized backprojection (CFBP) algorithm, a modified CFBP algorithm is proposed for SAR imaging to improve the accuracy and efficiency of processing. Besides, the influence of residual error due to mismatch is analyzed in detail. In the end, the simulation experiment and Gaofen-3 (GF-3) data experiment are carried out to demonstrate the feasibility of the proposed algorithm. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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19 pages, 5942 KiB

Open AccessArticle

A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization

by Saori Takeyama, Shunsuke Ono and Itsuo Kumazawa

Remote Sens. 2020, 12(21), 3541; https://doi.org/10.3390/rs12213541 - 28 Oct 2020

Cited by 13 | Viewed by 3494

Abstract

We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problems, consisting of a regularization term(s) and a data-fidelity term(s). The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objective function; therefore, we need to carefully control the hyperparameter(s) that balances these terms. However, the setting of such hyperparameters is often a troublesome task because their suitable values depend strongly on the regularization terms adopted and the noise intensities on a given observation. Our proposed method is formulated as a convex optimization problem, utilizing a novel hybrid regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV) and incorporating data-fidelity as hard constraints. HSSTV has a strong noise and artifact removal ability while avoiding oversmoothing and spectral distortion, without combining other regularizations such as low-rank modeling-based ones. In addition, the constraint-type data-fidelity enables us to translate the hyperparameters that balance between regularization and data-fidelity to the upper bounds of the degree of data-fidelity that can be set in a much easier manner. We also develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to efficiently solve the optimization problem. We illustrate the advantages of the proposed method over various HS image restoration methods through comprehensive experiments, including state-of-the-art ones. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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18 pages, 515 KiB

Open AccessArticle

A Bistatic Analytical Approximation Model for Doppler Rate Estimation Error from Real-Time Spaceborne SAR Onboard Orbit Determination Data

by Xiaoyu Yan, Jie Chen, Holger Nies and Otmar Loffeld

Remote Sens. 2020, 12(19), 3156; https://doi.org/10.3390/rs12193156 - 25 Sep 2020

Cited by 1 | Viewed by 2649

Abstract

Real-time spaceborne bistatic SAR imaging could significantly reduce the whole processing time and can enhance the spaceborne SAR mission availability. Onboard real-time SAR imaging relies on the Doppler parameters estimated from the real-time onboard orbit determination system (OODS) measurement, whose accuracy level is not comparable to the orbit ephemeris data in ground-based SAR processing. The investigation of the impact of error in real-time OODS measurements on bistatic SAR image quality is necessary, and it can help to clarify the key parameter limits of the real-time OODS. The monostatic analytical approximation model (MonoAAM) for spaceborne SAR reduces simulation complexity and processing time compared to the widely used numerical simulation method. However, due to the different configurations between spaceborne bistatic and monostatic SAR, simply applying the MonoAAM on spaceborne bistatic SAR cannot guarantee the desired result. A bistatic analytical approximation model (BiAAM) for Doppler rate estimation error from real-time OODS measurement in real-time spaceborne bistatic SAR imaging is proposed for characterizing the estimation error. Selecting quadratic phase error (QPE) as an evaluation variable, the proposed BiAAM model can provide QPE estimation results for each position of the satellite in its orbit and the maximum QPE estimation for the whole orbit, while revealing the different process of OODS measurement error transferring to QPE in spaceborne bistatic SAR. The correctness and reliability of BiAAM are evaluated by comparing the result with a Monte Carlo numerical simulation method. With the supporting result from BiAAM, the concept and early-stage development of a real-time onboard bistatic SAR imaging mission could be possibly benefited. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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22 pages, 4309 KiB

Open AccessArticle

Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target

by Ke Ren, Lan Du, Xiaofei Lu, Zhenyu Zhuo and Lu Li

Remote Sens. 2020, 12(17), 2766; https://doi.org/10.3390/rs12172766 - 26 Aug 2020

Cited by 15 | Viewed by 2776

Abstract

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR). Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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21 pages, 8610 KiB

Open AccessArticle

Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data

by Vassiliki Markogianni, Dionissios Kalivas, George P. Petropoulos and Elias Dimitriou

Remote Sens. 2020, 12(13), 2087; https://doi.org/10.3390/rs12132087 - 29 Jun 2020

Cited by 16 | Viewed by 4510

Abstract

Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 8 Operational Land Imager (OLI) sensors. The first step of the methodology involves the implementation of stepwise multiple regression (MLR) analysis of the available Chl-a dataset. Then, principal component analysis (PCA) is performed to explore Greek lakes’ potential interrelationships based on their Chl-a values in conjunction with certain criteria: their characteristics (artificial/natural), typology, and climatic type. Additionally, parameters such as seasonal water sampling and the date difference between sampling and satellite overpass are taken into consideration. Next, is implemented a stepwise multiple regression analysis among different groups of cases, formed by the criteria indicated from the PCA itself. This effort aimed at exploring different remote sensing-derived Chl-a algorithms for various types of lakes. The practical use of the proposed approach was evaluated in a total of 50 lake water bodies (natural and artificial) from 2013–2018, constituting the National Lake Network Monitoring of Greece in the context of the Water Framework Directive (WFD). All in all, the results evidenced the suitability of Landsat data when used with the proposed technique to estimate log-transformed Chl-a. The proposed scheme resulted in the development of models separately for natural (R = 0.78; RMSE = 1.3 μg/L) and artificial lakes (R = 0.76; RMSE = 1.29 μg/L), while the model developed without criteria proved weaker (R = 0.65; RMSE = 1.85 μg/L) in comparison to the other ones examined. The methodological framework proposed herein can be used as a useful resource toward a continuous monitoring and assessment of lake water quality, supporting sustainable water resources management. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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14 pages, 5005 KiB

Open AccessTechnical Note

Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers

by Xiaocheng Yang, Zhenyi Yang, Jingye Yan, Lin Wu and Mingfeng Jiang

Remote Sens. 2021, 13(3), 382; https://doi.org/10.3390/rs13030382 - 22 Jan 2021

Cited by 11 | Viewed by 2301

Abstract

Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method. Full article

(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)

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