1931-3195
Published by: SPIE
https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing?SSO=1
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Environmental Sciences | 443 | 48 | 73 | 11 |
| Computer Science | 511 | 61 | 59 | 11 |
The objective of Journal of Applied Remote Sensing is to combine knowledge in the areas of Remote sensing, Artificial intelligence, Contextual image classification, Pattern recognition and Synthetic aperture radar. The work on Remote sensing tackled in the journal brings together disciplines like Image resolution, Land cover, Satellite and Vegetation. The study on Satellite presented is investigated in conjunction with research in Meteorology.
While work presented in it provided substantial information on Vegetation, it also covered topics in Hydrology, Canopy, Satellite imagery and Physical geography. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Computer vision. Issues in Contextual image classification were discussed, taking into consideration concepts from other disciplines like Data mining and Support vector machine.
Topics in Pattern recognition explored in Journal of Applied Remote Sensing were investigated in conjunction with research in Data modeling, Pixel, Image fusion and Cluster analysis. Image fusion research presented in Journal of Applied Remote Sensing encompasses a variety of subjects, including Multispectral image and Sensor fusion. Synthetic aperture radar research featured in the journal incorporates concerns from various other topics such as Radar, Inverse synthetic aperture radar, Radar imaging and Algorithm.
The published papers are organized to reinforce research efforts on Remote sensing, Vegetation, Artificial intelligence, Contextual image classification and Hyperspectral imaging. The journal articles explore issues in Remote sensing which can be linked to other research areas like Image resolution, Satellite and Normalized Difference Vegetation Index. In addition to Artificial intelligence research, the journal articles aim to explore topics under Computer vision and Pattern recognition.
Journal of Applied Remote Sensing investigates studies in Artificial intelligence, Remote sensing, Pattern recognition, Hyperspectral imaging and Contextual image classification. While the journal focused on Remote sensing, it was also able to explore topics like Data modeling, Image resolution, Image fusion and Vegetation. The journal addresses concerns in Vegetation which are intertwined with other disciplines, such as Land cover, Climate change, Wetland and Ecosystem services.
In addition to Pattern recognition research, Journal of Applied Remote Sensing aims to explore topics under Synthetic aperture radar and Associative array. Journal of Applied Remote Sensing facilitates discussions on Hyperspectral imaging that incorporate concepts from other fields like Signal-to-noise ratio, Algorithm and Spatial analysis. The Contextual image classification works featured in the journal incorporate elements from Classifier (linguistics), Random forest, Support vector machine, Transfer of learning and Machine learning.
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.
The top authors publishing in Journal of Applied Remote Sensing (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Applied Remote Sensing (based on the number of publications) are:
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.
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, 9.45% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 13.74% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.40% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.13% of all publications and 63.74% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Sima Peyghambari;Yun Zhang
(2021)David C. Mason;Sarah L. Dance;Hannah L. Cloke;Hannah L. Cloke
(2021)Ele Vahtmäe;Birgot Paavel;Tiit Kutser
(2020)Annus Zulfiqar;Muhammad M. Ghaffar;Muhammad Shahzad;Christian Weis
(2021)Longfei Zhou;Shu Cheng;Qian Sun;Xiaohe Gu
(2020)Exploring computer science often leads students to consider related fields that complement and expand their skillset. For example, pursuing an online degree in mechanical engineering can open doors to opportunities in robotics and automation, blending physical systems with computational design.
Similarly, a bachelor of science in physics online offers a strong foundation in problem-solving and analytical thinking, which are critical in areas like quantum computing and software development.
Data-driven roles continue to grow, making an affordable data science degree a popular choice for students interested in extracting meaningful insights from large datasets, a key skill in technology companies.
For those aiming to advance their expertise, an online master’s in electrical engineering degree provides advanced knowledge applicable in developing cutting-edge hardware and embedded systems, which are integral to many computer science applications.
These related online degrees expand potential career pathways and allow learners to tailor their educational journey to evolving industry demands.
