IEEE Geoscience and Remote Sensing Letters

Top Research Topics at IEEE Geoscience and Remote Sensing Letters?

Artificial intelligence, Remote sensing, Pattern recognition, Synthetic aperture radar and Computer vision are the subjects of interest in IEEE Geoscience and Remote Sensing Letters. Feature extraction, Hyperspectral imaging, Pixel, Convolutional neural network and Image resolution are all aspects of Artificial intelligence research featured in IEEE Geoscience and Remote Sensing Letters. It links adjacent topics like Feature extraction with Deep learning.

The Remote sensing research presented in the journal explores the relationship between Radar and the closely related topic of Doppler effect. While work presented in IEEE Geoscience and Remote Sensing Letters provided substantial information on Pattern recognition, it also covered topics in Contextual image classification, Image (mathematics) and Feature (computer vision). The work on Synthetic aperture radar tackled in it brings together disciplines like Inverse synthetic aperture radar, Radar imaging, Algorithm and Interferometry.

It connects the study in Radar imaging with the closely related area of Optics. Research on Computer vision presented in the journal focuses, in particular, on Iterative reconstruction, Object detection and Multispectral image. Issues in Continuous-wave radar were discussed, taking into consideration concepts from other disciplines like Radar engineering details and Pulse-Doppler radar.

• Artificial intelligence (40.47%)
• Remote sensing (31.46%)
• Pattern recognition (24.75%)

What are the most cited papers published in the journal?

• A Landsat surface reflectance dataset for North America, 1990-2000 (1061 citations)
• Composite kernels for hyperspectral image classification (850 citations)
• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (616 citations)

Research areas of the most cited articles at IEEE Geoscience and Remote Sensing Letters:

The most cited articles are mainly concerned with subjects like Artificial intelligence, Pattern recognition, Remote sensing, Computer vision and Synthetic aperture radar. The published papers explore research in Meteorology and overlapping concepts in Moderate-resolution imaging spectroradiometer to expand the discourse in Remote sensing. The works on Synthetic aperture radar tackled in the journal papers bring together disciplines like Inverse synthetic aperture radar, Radar imaging, Bistatic radar, Interferometry and Algorithm.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Optics

The previous edition focused in particular on these issues:

The concepts of Artificial intelligence, Pattern recognition, Remote sensing, Algorithm and Synthetic aperture radar are tackled in the journal. Artificial intelligence and Computer vision are closely related fields of research discussed in it. In addition to Pattern recognition research, IEEE Geoscience and Remote Sensing Letters aims to explore topics under Artificial neural network, Pixel, Image (mathematics) and Feature (computer vision).

While the journal focused on Remote sensing, it was also able to explore topics like Object detection and Satellite. It facilitates discussions on Algorithm that incorporate concepts from other fields like Radar and Signal. Some problems in Synthetic aperture radar that were presented in it overlapped with concepts under Clutter and Azimuth.

The most cited articles from the last journal are:

• Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification (26 citations)
• Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning (17 citations)
• SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images (17 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Geoscience and Remote Sensing Letters (based on the number of publications) are:

• Mengdao Xing (62 papers) published 7 papers at the last edition, 1 more than at the previous edition,
• Robert Wang (55 papers) published 8 papers at the last edition, 3 more than at the previous edition,
• Licheng Jiao (55 papers) published 4 papers at the last edition, 2 less than at the previous edition,
• Zheng Bao (55 papers) published 2 papers at the last edition, 1 less than at the previous edition,
• Qian Du (51 papers) published 10 papers at the last edition, 6 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Geoscience and Remote Sensing Letters (based on the number of publications) are:

• Chinese Academy of Sciences (400 papers) published 100 papers at the last edition, 40 more than at the previous edition,
• Xidian University (329 papers) published 40 papers at the last edition, 14 more than at the previous edition,
• Wuhan University (296 papers) published 54 papers at the last edition, 23 more than at the previous edition,
• National University of Defense Technology (186 papers) published 28 papers at the last edition, 9 more than at the previous edition,
• Tsinghua University (95 papers) published 14 papers at the last edition, 5 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 26.53% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 37.84% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.76% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.63% of all publications and 31.76% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Potential Future Research Directions

While the study has comprehensively analyzed various topical research areas in the IEEE Geoscience and Remote Sensing Letters, it is crucial to highlight potential future trends that could provide further impetus to the ongoing work in the domain. As technology advances, new topics and techniques are emerging with potential for wide-ranging impact on the areas of artificial intelligence, pattern recognition, and remote sensing among others. Such novel areas could include deep reinforcement learning, generative models, quantum computing, edge computing and the Internet of Things (IoT).

Deep reinforcement learning has gained significant attention for its ability to learn complex patterns and make decisions based on those patterns. This could be harnessed in remote sensing and pattern recognition to improve the accuracy and efficacy of existing techniques.

Beyond these, the proliferation of IoT devices means more data sources for remote sensing and geoscience. How to use this data effectively remains a promising area of research. Additionally, with the advent of quantum computing, researchers may need to start considering its potential applications in these fields.

Besides, it is noteworthy to acknowledge the necessity of having a strong foundational knowledge and specific certifications in the field of research. For instance, positions such as a research assistant or a teaching assistant may need specific requirements or skills. A good example to reference would be the teacher assistant certificate requirements in texas. Adapting these expertise and requirements to the field of research could potentially drive possible advancements and transitions.

Overall, continuing research in these areas could not only lead to the development of innovative AI technologies but also contribute significantly to the scientific community, aiding in high impact works in the domain of geoscience and remote sensing.