# Foreword to the Special Issue on Recent Advances in Processing of High-Spatial-Resolution Remote Sensing Data

SINCE the launch of the first high-spatial-resolution (HSR) remote sensing satellite (IKONOS, in September 1999), we have been entering a new era of high-resolution earth observation (EO). A variety of high-resolution remote sensing platforms continue to emerge. For instance, the new generation of the WorldView-3/4 satellites can provide high-resolution and super-spectral images, and their extraordinary ability allows them to simultaneously and accurately depict the spectral-spatial properties of the land surface. The HSR imaging radar (SAR) satellites, e.g., Cosmo-SkyMed, TerraSAR-X, and Gaofen-3, are capable of gathering high-resolution information independent of weather conditions. In particular, ZY-3, China's first civilian high-resolution satellite, carries high-resolution multiview cameras, including forward, nadir, and backward modes [items 1) and 2) in the Appendix]. This notable advantage enables us to obtain three-dimensional (3-D) EO data, and facilitates a number of new applications. Smart satellites (e.g., planet labs) have to be mentioned due to their high revisit frequency, i.e., achieving daily coverage of the earth. In addition, unmanned aerial vehicles (UAVs) have also received increasing attention, thanks to their timely, flexible, and efficient data acquisition capacity.

In this context, advanced processing methods for HSR imagery are of great interest, e.g., HSR multisensor fusion [item 3) in the Appendix], deep change analysis [item 4) in the Appendix], deep learning [items 5)–7) in the Appendix], and in the meantime, a series of new application scenarios are continuing to appear, such as building height learning and estimation [items 8) and 9) in the Appendix], social functional mapping [items 10) and 11) in the Appendix], local climate zones classification [item 12) in the Appendix], slum mapping [item 13) in the Appendix], large-scale object detection and tracking [items 14) and 15) in the Appendix], monitoring ecosystem service [item 16) in the Appendix], and 3-D urban heat island interpretation [item 17) in the Appendix]. Therefore, it is timely for this special issue to exhibit and discuss the recent advances in HSR remote sensing.

This issue intends to introduce recent advances in the HSR data processing. It contains 15 papers, mainly concerning the preprocessing (one paper), deep learning (four papers), superresolution (two papers), object recognition (three papers), object-based image analysis (OBIA) (two papers), and applications (three papers).

## I. PREPROCESSING

Gholinejad et al. [item 18) in the Appendix] focused on the geometrical correction of HSR imagery. The traditional rational function model is usually subject to the overparameterization problem owing to their highly correlated coefficients. To this end, recently, a series of algorithms have been proposed to cope with this issue, e.g., the particle swarm optimization (PSO), aiming to find the optimal coefficients. However, the performance of the algorithm can be influenced by the initialization and the limited number of ground control points. Therefore, Wurm et al. [item 13) in the Appendix] proposed a modified PSO method by considering the k-fold cross validation. Their experimental results validated the robustness of the method and its superiority to the state-of-the-art ones.

## II. DEEP LEARNING

In this issue, it is not surprising that the topic of deep learning attracts the most attention. Du et al. [item 19) in the Appendix] attempted to develop and improve the pretrained convolutional neural networks (CNN) for feature representations. Specifically, two subspace learning methods were used to extract the multi-layer features from a single pretrained CNN and then extend it to the multi-CNN models for classifying two public and challenging HSR datasets. Their results demonstrated the effectiveness of the transferred pretrained networks and the proposed fusion strategy for scene classification, compared to the conventional methods.

Peng et al. [item 20) in the Appendix] proposed a series of strategies to modify and refine the convolutional networks, considering the limitations of the existing CNN models, e.g., gradient explosion and overfitting. First, they formed a densely based fully convolutional networks (DFCNs) by combining dense connection and fully convolutional networks. The DFCN was then strengthened via a set of multiscale filters to enrich the extracted features, which are more robust than the original convolutional information. In addition, the authors also tested the effectiveness of the proposed networks by integrating the digital surface models information into the model. Their experimental results were assessed on the public dataset for semantic segmentation, with comparable accuracy achieved compared to the state-of-the-art algorithms.

Object-based building extraction is an active research topic, but it is usually subject to the issues of oversegmentation and scale parameter. Meanwhile, it is difficult to deal with the

<footnote>Digital Object Identifier 10.1109/JSTARS.2019.2936980</footnote>
transferability of the object-based image processing methods, considering the complexity of the segmentation results. In this context, Majd et al. [item 21] in the Appendix] proposed an object-based deep CNN model and attempted to address the aforementioned problems by selecting the optimal CNN from a series of configurations of CNNs. The effectiveness of the proposed method was shown in the Vaihingen dataset, and its transferability was validated in a different sensor (WorldView-2).

Although CNNs have obtained much success for remote sensing image classification, the current models are seemingly becoming increasingly complex by combining multiple networks or feature sources. In this context, Zhang et al. [item 22] in the Appendix] designed a lightweight CNN based on MobileNet V2, and the dilated convolution and channel attention were adopted for feature extraction. In addition, the multiscale features were represented using a multidilation pooling module. Six datasets were used to validate the proposed networks with comparable accuracy obtained to the existing mainstream methods.

### III. SUPERRESOLUTION

Two papers are concerning superresolution. One lays particular attention to a specific sensor (GF4 satellite), but the other emphasizes the reconstruction method. Wang et al. [item 23] in the Appendix] addressed the superresolution of GF-4, which is the first civilian geostationary orbit high-resolution satellite of China, using the multi-image approach. The basic idea of the proposed method is to fuse the original spectral images with the reconstructed high-resolution texture information achieved by the projection onto convex sets algorithm. The proposed method can obtain higher reconstruction quality in terms of a set of quantitative metrics. Shao et al. [item 24] in the Appendix] addressed the superresolution, and they proposed a coupled sparse autoencoder method to learn the relation between the sparse coefficients derived from the high-resolution and low-resolution images.

### IV. OBJECT RECOGNITION

The papers for object recognition refer to buildings, shadow, and tailings ponds. It is interesting to see that the three research papers adhere to the traditional routes, i.e., feature representation, but do not involve deep learning.

Hao et al. [item 25] in the Appendix] proposed an active building extraction method, on the basis of visual perception theory. Specifically, they presented that the main direction and color information can reflect the characteristics of the building outline, and subsequently, the textural and geometrical features can be used to detect buildings accurately.

Fang et al. [item 26] in the Appendix] presented a shadow index, based on the normalized difference between the saturation and near infrared channel. The basic idea is that shadow regions usually show small response in the near-infrared band, but other objects (e.g., vegetation, buildings, and roads) do not. But the authors admitted that the proposed shadow index should be further tested in other sensors and more complicated image scenes.

Liu et al. [item 27] in the Appendix] concerned the detection of the tailings ponds, owing to their threat to neighboring inhabitants and ecological environments. Conventionally, the monitoring of the tailings ponds was difficult, and relied on manual visual interpretation, considering their heterogeneous and complicated spatial composition. In this research, the authors attempted to identify the targets by investigating the spatial combinations of their main structures, including starter dams, embankments, deposited beach, and water body. After several steps of postprocessing, satisfactory results were obtained, achieving 88%–97% overall accuracy, which was significantly higher than the random forest classifier.

### V. OBJECT-BASED IMAGE ANALYSIS

OBIA is a classic problem for HSR data processing. It refers to a series of research points, e.g., segmentation, feature extraction, rule-based classification, machine learning, and object recognition. In this special issue, we have two papers for OBIA, with one for the segmentation quality assessment and the other for the segmentation algorithm. Notice that in Section II, there is one study trying to combine OBIA and deep learning.

Wu et al. [item 28] in the Appendix] proposed a hierarchical segmentation evaluation method for evaluating the segmentation quality. This is an important issue for OBIA, and the authors claimed that their method can be further used to select and optimize the scale parameter. Zheng et al. [item 29] in the Appendix] developed a hybrid Markov random field model by taking the multigranularity information into account, for segmentation of HSR imagery. This idea is based on the fact that the current segmentation methods often consider single-layer granularity, e.g., pixel or object. Specifically, the different granularities information is represented and integrated in a multilayer probability graph.

### VI. APPLICATIONS

Fan et al. [item 30] in the Appendix] employed GF-3, the first full polarimetric synthetic aperture radar satellite of China, for detecting the marine floating raft aquaculture. In order to strengthen the detection accuracy, an unsupervised collective multikernel fuzzy C-means (CMK-FCM) algorithm was proposed to deal with the SAR and its textural features. Their experiments revealed the satisfactory performances of GF-3 and the proposed multifeature fusion achieved by the proposed CMK-FCM algorithm.

As a typical application scenario for disaster response, high-resolution imagery has potential for monitoring and evaluating the damaged buildings in an earthquake. Liu and Li [item 31] in the Appendix] proposed a new object-based homogeneous index as well as an object histogram approach in order to extract collapsed buildings. The proposed method is based on the consideration that the intact objects show substantially distinct characteristics compared the collapsed buildings. The bitemporal QuickBird images, acquired for the earthquake of Dam, Iran, in 2003, were used to assess the performance of the proposed strategy. In their experiments, the efficiency has been validated.
Zhang et al. [item 32] in the Appendix] investigated the extraction and segmentation of glacial lakes on the basis of SAR data, considering its all-weather observation ability, i.e., regardless of weather and solar conditions. The current difficulties for SAR-based lake detection lie in the noise and low contrast. To cope with these issues, the authors presented a phase-congruency-based detector, which is able to represent the geometrical and textural features of the glacial lakes. Their experiments were conducted on the Sentinel-1A/1B images of the Himalayas, with accurate segmentation achieved, i.e., the relative errors were smaller than four pixels for all the lakes.

X. HUANG, Guest Editor
School of Remote Sensing
and Information Engineering
Wuhan University
Wuhan 430079, China

X. X. ZHU, Guest Editor
Signal Processing in Earth Observation
Technical University of Munich
80333 Munich, Germany

F. DELL'ACQUA, Guest Editor
Department of Electrical, Computer
and Biomedical Engineering
University of Pavia
I-27100 Pavia, Italy

M. FAUVEL, Guest Editor
CESBIO
Université de Toulouse,
CNES/CNRS/INRA/IRD/UPS/IRD/UPS
Toulouse, France

M. DALLA MURA, Guest Editor
CNRS, Grenoble INP, GIPSA-lab,
University Grenoble Alpes
38000 Grenoble, France
Tokyo Tech World Research Hub Initiative
School of Computing
Tokyo Institute of Technology
Tokyo 152-8552, Japan

F. LOMBARDINI, Guest Editor
Department of “Ingegneria
dell’Informazione”
University of Pisa
56122 Pisa, Italy

APPENDIX
RELATED WORK

1) X. Huang, D. Wen, J. Li, and R. Qin, "Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery," *Remote Sens. Environ.*, vol. 196, no. 1, pp. 56–75, Jul. 2017.

2) C. Liu, X. Huang, Z. Zhu, H. Chen, X. Tang, and J. Gong, "Automatic extraction of built-up area from ZY3 multi-view satellite imagery: Analysis of 45 global cities," *Remote Sens. Environ.*, vol. 226, no. 1, pp. 51–73, Jun. 2019.

3) P. P. Aravena, K. Spröhne, C. Geiß, E. Schoepfer, S. Plank, and H. Taubenböck, "Multi-sensor feature fusion for very HSR built-up area extraction in temporary settlements," *Remote Sens. Environ.*, vol. 209, no. 1, pp. 793–807, May 2018.

4) S. Saha, F. Bovolo, and L. Bruzzone, "Unsupervised deep change vector analysis for multiple-change detection in VHR images," *IEEE Trans. Geosci. Remote Sens.*, vol. 57, no. 6, pp. 3677–3693, Jun. 2019.

5) J. Li, X. Huang, and J. Gong, "Deep neural network for remote-sensing image interpretation: Status and perspectives," *Nat. Sci. Rev.*, May 2019, doi: 10.1093/nsr/nwz058.

6) X. Zhu et al., "Deep learning in remote sensing: A comprehensive review and list of resources," *IEEE Geosci. Remote Sens. Mag.*, vol. 5, no. 4, pp. 8–36, Dec. 2017.

7) S. Srivastava, J. E. Vargas-Muñoz, and D. Tuia, "Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution," *Remote Sens. Environ.*, vol. 228, no. 1, pp. 129–143, Jul. 2019.

8) Y. Shi, Q. Li, and X. Zhu, "Building footprint generation using improved generative adversarial networks," *IEEE Geosci. Remote Sens. Lett.*, vol. 16, no. 4, pp. 603–607, Apr. 2019.

9) H. A. Amirkolaee and H. Arefi, "Height estimation from single aerial images using a deep convolutional encoder-decoder network," *ISPRS J. Photogramm. Remote Sens.*, vol. 149, no. 1, pp. 50–66, Mar. 2019, doi: 10.1093/nsr/nwz058.

10) J. Kang, M. Körner, Y. Wang, H. Taubenböck, and X. Zhu, "Building instance classification using street view images," *ISPRS J. Photogramm. Remote Sens.*, vol. 145, pp. 44–59, 2018.

11) W. Chen, H. Huang, J. Dong, Y. Zhang, Y. Tian, and Z. Yang, "Social functional mapping of urban green space using remote sensing and social sensing data," *ISPRS J. Photogramm. Remote Sens.*, vol. 146, no. 1, pp. 436–452, Dec. 2018.

12) C. Qiu, L. Mou, M. Schmitt, and X. Zhu, "Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network," *ISPRS J. Photogramm. Remote Sens.*, vol. 154, no. 1, pp. 151–162, Aug. 2019.

13) M. Wurm, T. Stark, X. Zhu, M. Weigand, and H. Taubenböck, "Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks," *ISPRS J. Photogramm. Remote Sens.*, vol. 150, pp. 59–69, 2019.

14) H. L. Yang, J. Yuan, D. Lunga, M. Laverdiere, A. Rose, and B. Bhaduri, "Building extraction at scale using convolutional neural network: Mapping of the United States," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 11, no. 8, pp. 2600–2614, Aug. 2018.
15) Q. Li, L. Mou, Q. Xu, Y. Zhang, X. Zhu, "R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos," *IEEE Trans. Geosci. Remote Sens.*, vol. 57, no. 7, pp. 5028-5042, Jul. 2019.

16) X. Huang, X. Han, S. Ma, T. Lin, and J. Gong, "Monitoring ecosystem service change in the city of Shenzhen by the use of high-resolution remotely sensed imagery and deep learning," *Land Degradation Develop.*, vol. 30, no. 12, pp. 1490-1501, Jul. 2019.

17) X. Huang and Y. Wang, "Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China," *ISPRS J. Photogramm. Remote Sens.*, vol. 152, no. 1, pp. 119-131, Jun. 2019.

18) S. Gholinejad, A. A. Naeini, and A. Amiri-Simkooei, "Robust particle swarm optimization of RFMS for high-resolution satellite images based on K-fold cross-validation," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

19) P. Du, E. Li, J. Xia, A. Samat, and X. Bai, "Feature and model level fusion of pretrained CNN for remote sensing scene classification," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

20) C. Peng, Y. Li, L. Jiao, Y. Chen, and R. Shang, "Densely based multi-scale and multi-modal fully convolutional networks for high-resolution remote-sensing image semantic segmentation," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

21) R. D. Majd, M. Momeni, and P. Moallem, "Transferable object-based framework based on deep convolutional neural networks for building extraction," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

22) B. Zhang, Y. Zhang, and S. Wang, "A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

23) M. Wang, L. He, X. Chang, and Y. Cheng, "Superresolution of single Gaofen-4 visible-light and near-infrared (VNIR) image based on texture image extraction," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

24) Z. Shao, L. Wang, Z. Wang, and J. Deng, "Remote sensing image super-resolution using sparse representation and coupled sparse autoencoder," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

25) L. Hao, Y. Zhang, and Z. Cao, "Active cues collection and integration for building extraction with high-resolution color remote sensing imagery," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

26) H. Fang, Y. Wei, H. Luo, and Q. Hu, "Detection of building shadow in remote sensing imagery of urban areas with fine spatial resolution based on saturation and near-infrared information," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

27) K. Liu, R. Liu, and Y. Liu, "A tailings pond identification method based on spatial combination of objects," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

28) Z. Wu, L. He, Z. Hu, Y. Zhang, and G. Wu, "Hierarchical segmentation evaluation of region-based image hierarchy," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

29) C. Zheng, L. Wang, and X. Chen, "A hybrid Markov random field model with multi-granularity information for semantic segmentation of remote sensing imagery," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

30) J. Fan, J. Zhao, W. An, and Y. Hu, "Marine floating raft aquaculture detection of GF-3 polsar images based on collective multikernel fuzzy clustering," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

31) J. Liu and P. Li, "Extraction of earthquake-induced collapsed buildings from bi-temporal VHR images using object-level homogeneity index and histogram," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.

32) M. Zhang, F. Chen, B. Tian, and D. Liang, "Using a phase-congruency-based detector for glacial lake segmentation in high-temporal resolution Sentinel-1A/1B data," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 12, no. 8, Aug. 2019.
<figure><img src="image_1.png" /></figure>

**Xin Huang** (M'13-SM'14) received the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2009.

He is currently a Luojia Distinguished Professor with Wuhan University, where he teaches remote sensing, photogrammetry, image interpretation, etc. He is with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing. He is the Founder and Director of the Institute of Remote Sensing Information Processing, School of Remote Sensing and Information Engineering, Wuhan University. He has authored or coauthored more than 130 peer-reviewed articles (SCI papers) in international journals. His research interests include remote sensing image processing methods and applications.

Prof. Huang has been supported by the National Program for Support of Top-Notch Young Professionals (2017), the China National Science Fund for Excellent Young Scholars (2015), and the New Century Excellent Talents in University from the Ministry of Education of China (2011). He was the recipient of the Boeing Award for the Best Paper in Image Analysis and Interpretation

from the American Society for Photogrammetry and Remote Sensing (ASPRS) in 2010, the second place recipient for the John I. Davidson President's Award from ASPRS in 2018, and the National Excellent Doctoral Dissertation Award of China in 2012. In 2011, he was recognized by the IEEE Geoscience and Remote Sensing Society (GRSS) as the Best Reviewer of the IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. He was the winner of the IEEE GRSS 2014 Data Fusion Contest. He was the Lead Guest Editor of the Special Issue on Information Extraction From High-Spatial-Resolution Optical Remotely Sensed Imagery for the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (vol. 8, no. 5, May 2015), and the Lead Guest Editor of the Special Issue on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis for the Journal of Applied Remote Sensing (vol. 10, no. 4, Oct. 2016). He has been an Associate Editor of Photogrammetric Engineering and Remote Sensing since 2016, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS since 2014, and IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING since 2018. He has also been an Editorial Board Member of Remote Sensing of Environment since 2019, and Remote Sensing (an open access journal from MDPI) since 2018.

<figure><img src="image_2.png" /></figure>

**Xiao Xiang Zhu** (S'10-M'12-SM'14) received the bachelor's degree in space engineering from the National University of Defense Technology, Changsha, China, in 2006, and the M.Sc., Dr.-Ing., and Habilitation degrees in signal processing from the Technical University of Munich (TUM), Munich, Germany, in 2008, 2011, and 2013, respectively.

She is currently a Professor for Signal Processing in Earth Observation with TUM and German Aerospace Center (DLR), the Head of the Department "EO Data Science," DLR's Earth Observation Center, and the Head of the Helmholtz Young Investigator Group "SiPEO" at DLR and TUM. Since 2019, she has been co-coordinating the Munich Data Science Research School. She is also leading the Helmholtz Artificial Intelligence Cooperation Unit-Research Field "Aeronautics, Space and Transport." She was a Guest Scientist or Visiting Professor with the Italian National Research Council (CNR-IREA), Naples, Italy, in 2009; Fudan University, Shanghai, China, in 2014; the University of Tokyo, Tokyo, Japan, in 2015; and the University of California, Los Angeles, Los Angeles, CA, USA, in 2016. Her main research interests include

remote sensing and earth observation, signal processing, machine learning and data science, with a special application focus on global urban mapping.

Dr. Zhu is a member of Young Academy (Junge Akademie/Jungen Kolleg) at the Berlin-Brandenburg Academy of Sciences and Humanities, the German National Academy of Sciences Leopoldina, and the Bavarian Academy of Sciences and Humanities. She is an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.

<figure><img src="image_3.png" /></figure>

**Fabio Dell'Acqua** received the graduate degree (Hons.) cum laude in electronics engineering and the Ph.D. degree in remote sensing from the University of Pavia, Pavia, Italy, in 1996 and 1999, respectively.

He is an Associate Professor of remote sensing with the University of Pavia, qualified for full professorship. In 2000, he was an Associate Researcher with the University of Edinburgh, U.K., while in 2001, he obtained a permanent position as an Assistant Professor at the University of Pavia. He has more than 20 years of experience in earth observation and signal processing. He taught remote sensing courses at various international venues. He has also established strong links with companies in the business, which he exploits to create growth and employment opportunities for his graduating and graduated students. He took part in several international research projects on the topics of his research interests, which include detection and classification of objects in remotely sensed data, and the use of remote sensing for risk management purposes.

Dr. Dell'Acqua has been an Associate Editor and Guest Editor for various journals since 2008.
<figure><img src="image_4.png" /></figure>

**Mauro Dalla Mura** (S'08–M'11–SM'18) received the B.Sc. and M.Sc. degrees in telecommuni-
cation engineering from the University of Trento, Trento, Italy, in 2005 and 2007, respectively, and
the joint Ph.D. degree in information and communication technologies (telecommunications area)
from the University of Trento, and in electrical and computer engineering from the University of
Iceland, Reykjavik, Iceland, in 2011.

In 2011, he was a Research Fellow with Fondazione Bruno Kessler, Trento, and conducted research on computer vision. He is currently an Assistant Professor with the Grenoble Institute of Technology (Grenoble INP), France, since 2012. He is conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab). He is appointed "Specially Appointed Associate Professor" at the School of Computing, Tokyo Institute of Technology, Japan, for 2019–2022. His main research interests include remote sensing, image processing and pattern recognition, with focuses on mathematical morphology, classification and multivariate data analysis.

Dr. Dalla Mura was the recipient of the IEEE Geoscience and Remote Sensing Society (GRSS) Second Prize in the Student Paper Competition of the 2011 IEEE IGARSS 2011 and corecipient of the Best Paper Award of the *International Journal of Image and Data Fusion* for the year 2012–2013 and the Symposium Paper Award for IEEE IGARSS 2014. He has been the President of the IEEE GRSS French Chapter since 2016 (he previously served as Secretary during 2013–2016). In 2017, the IEEE GRSS French Chapter was the recipient of the IEEE GRSS Chapter Award and the “Chapter of the year 2017” from the IEEE French Section. He has been with the Editorial Board of the *IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING* since 2016.

<figure><img src="image_5.png" /></figure>

**Mathieu Fauvel** received the degree in electrical engineering and the M.S. and Ph.D. degrees in image and signal processing from the Grenoble Institute of Technology, Grenoble, France, in 2004 and 2007, respectively.

In 2007, he was a Teaching Assistant with Grenoble INP. From 2008 to 2010, he was a Postdoctoral Research Associate with the MISTIS Team of the National Institute for Research in Computer Science and Control (INRIA). Since 2011, he has been an Associate Professor with the National Polytechnic Institute of Toulouse (ENSAT-University of Toulouse) within the DYNAFOR Laboratory (University of Toulouse-INRA). His research interests include remote sensing, data fusion, pattern recognition, multicomponent signal, and image processing.

Dr. Fauvel has been the President of the French Chapter of the GRSS since 2013.

**Fabrizio Lombardini** (M'93-SM'03) received the Italian Laurea degree (Hons.) in electronic engineering and the Ph.D. degree in telecommunication engineering from the University of Pisa, Pisa, Italy, in 1993 and 1997, respectively.

He was a Postdoctoral Researcher with the University College London, U.K., from 1998 to 1999, and then he joined the Department of Information Engineering, University of Pisa, where he currently holds the position of Senior Assistant Professor. He has been a Consultant with the National Laboratory of Radar and Surveillance Systems of the Italian Interuniversity Consortium of Telecommunications since 2011, and in 2013, received the National Scientific Habilitation of Associate Professor in Telecommunications. He designed the new mode of 4-D SAR imaging. He holds three patents and has participated in several national and international research projects related to the topics of his research interests, which include statistical signal processing; estimation and detection; superresolution and robust spectral analysis and array processing; sparse sampling; multiplicative noise; performance bound evaluation, with application to radar systems and SAR environmental remote sensing; multibaseline and multifrequency interferometric SAR algorithms and systems, both cross- and along-track; three-dimensional (3-D) SAR tomography; differential interferometry; multisensor data fusion; and localization in RFID systems.

Dr. Lombardini has chaired 20 special sessions on SAR multichannel interferometry and 3-D/multidimensional techniques at international conferences, and has been a Lecturer of nine tutorials on SAR tomography and multidimensional SAR imaging at EUSAR'08 and IEEE Remote Sensing Conferences. He has served as a Guest Co-Editor for the EURASIP *Journal of Applied Signal Processing* Special Issue on Advances in Interferometric SAR, and is an Associate Editor of the *IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATION AND REMOTE SENSING*.