Remote sensing big data for water environment monitoring: current status, challenges, and future prospects

J Chen, S Chen, R Fu, D Li, H Jiang, C Wang… - Earth's …, 2022 - Wiley Online Library
Accurate water extraction and quantitative estimation of water quality are two key and
challenging issues for remote sensing of water environment. Recent advances in remote …

A novel deep learning method for detection and classification of plant diseases

W Albattah, M Nawaz, A Javed, M Masood… - Complex & Intelligent …, 2022 - Springer
The agricultural production rate plays a pivotal role in the economic development of a
country. However, plant diseases are the most significant impediment to the production and …

Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification

SRG Reddy, GPS Varma, RL Davuluri - Computers and Electrical …, 2023 - Elsevier
The manual inspections of plant diseases resulted in low accuracy with high time
consumption and unable to predict the multiple diseases of plants. To address these …

Time series analysis for global land cover change monitoring: A comparison across sensors

L Xu, M Herold, NE Tsendbazar, D Masiliūnas… - Remote Sensing of …, 2022 - Elsevier
Comparing the performance of different satellite sensors in global land cover change (LCC)
monitoring is necessary to assess their potential and limitations for more accurate and …

[HTML][HTML] A full resolution deep learning network for paddy rice mapping using Landsat data

L Xia, F Zhao, J Chen, L Yu, M Lu, Q Yu, S Liang… - ISPRS Journal of …, 2022 - Elsevier
Rice is the most important food crop in the developing world, and more than half of the
global population consumes it as a staple food. Mapping the area of rice cultivation in a …

[HTML][HTML] Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification

L Wang, J Wang, Z Liu, J Zhu, F Qin - The Crop Journal, 2022 - Elsevier
High-resolution deep-learning-based remote-sensing imagery analysis has been widely
used in land-use and crop-classification mapping. However, the influence of composite …

[HTML][HTML] Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1/2 images

K Peng, W Jiang, P Hou, Z Wu, Z Ling, X Wang, Z Niu… - Ecological …, 2023 - Elsevier
Wetlands have suffered from considerable degradation due to anthropogenic and natural
disturbances in recent decades. Although some advancements have been made, effective …

PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping

S Acharki - Remote Sensing Applications: Society and …, 2022 - Elsevier
Up-to-date and accurate land use and land cover (LULC) maps are vital for monitoring
various environmental and natural resources management. In this study, we evaluate …

[HTML][HTML] Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial …

B Fu, M Liu, H He, F Lan, X He, L Liu, L Huang… - International Journal of …, 2021 - Elsevier
Karst wetlands have the characteristics of small scale and poor stability. At present, the
wetland is being severely damaged and its area is seriously degraded, and the accurate …

[HTML][HTML] 3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer

A Jamali, M Mahdianpari, B Brisco, D Mao, B Salehi… - Ecological …, 2022 - Elsevier
Many ecosystems, particularly wetlands, are significantly degraded or lost as a result of
climate change and anthropogenic activities. Simultaneously, developments in machine …