Advanced Machine Learning and Deep Learning Approaches for Remote Sensing-Reference-Cited by-同舟云学术

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

Published:2023-06-01 Issue:11 Volume:15 Page:2876
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Jeon Gwanggil¹²^ORCID

Affiliation:

1. Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea

2. Energy Excellence & Smart City Lab., Incheon National University, Incheon 22012, Republic of Korea

Abstract

Unlike field observation or field sensing, remote sensing is the process of obtaining information about an object or phenomenon without making physical contact [...]

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

https://www.mdpi.com/2072-4292/15/11/2876/pdf

Reference17 articles.

1. Ma, D., Wu, R., Xiao, D., and Sui, B. (2023). Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method. Remote Sens., 15.

2. Li, Y., Cheng, Z., Wang, C., Zhao, J., and Huang, L. (2023). RCCT-ASPPNet: Dual-Encoder Remote Image Segmentation Based on Transformer and ASPP. Remote Sens., 15.

3. Zhang, L., Leng, X., Feng, S., Ma, X., Ji, K., Kuang, G., and Liu, L. (2023). Azimuth-Aware Discriminative Representation Learning for Semi-Supervised Few-Shot SAR Vehicle Recognition. Remote Sens., 15.

4. Awad, M.M. (2023). FlexibleNet: A New Lightweight Convolutional Neural Network Model for Estimating Carbon Sequestration Qualitatively Using Remote Sensing. Remote Sens., 15.

5. Ravishankar, R., AlMahmoud, E., Habib, A., and de Weck, O.L. (2023). Capacity Estimation of Solar Farms Using Deep Learning on High-Resolution Satellite Imagery. Remote Sens., 15.