A review on multiscale-deep-learning applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …
models suffer from spatial-information loss and inadequate feature-representation issues …
Deep learning models for the classification of crops in aerial imagery: A review
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …
vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield …
A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images
As a critical source of food and one of the most economically significant crops in the world,
soybean plays an important role in achieving food security. Large area accurate mapping of …
soybean plays an important role in achieving food security. Large area accurate mapping of …
[HTML][HTML] Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series
Accurate crop distribution mapping is required for crop yield prediction and field
management. Due to rapid progress in remote sensing technology, fine spatial resolution …
management. Due to rapid progress in remote sensing technology, fine spatial resolution …
Crop mapping using supervised machine learning and deep learning: a systematic literature review
M Alami Machichi, E mansouri, Y Imani… - … Journal of Remote …, 2023 - Taylor & Francis
The ever-increasing global population presents a looming threat to food production. To meet
growing food demands while minimizing negative impacts on water and soil, agricultural …
growing food demands while minimizing negative impacts on water and soil, agricultural …
Agricultural field boundary delineation with satellite image segmentation for high-resolution crop mapping: A case study of rice paddy
Parcel-level cropland maps are an essential data source for crop yield estimation, precision
agriculture, and many other agronomy applications. Here, we proposed a rice field mapping …
agriculture, and many other agronomy applications. Here, we proposed a rice field mapping …
[HTML][HTML] Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery
Accurate crop mapping is of great significance for crop yield forecasting, agricultural
productivity development and agricultural management. Thanks to its all-time and all …
productivity development and agricultural management. Thanks to its all-time and all …
[HTML][HTML] Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network
Spectral and spatial features in remotely sensed data play an irreplaceable role in
classifying crop types for precision agriculture. Despite the thriving establishment of the …
classifying crop types for precision agriculture. Despite the thriving establishment of the …
Analysis of patch and sample size effects for 2D-3D CNN models using multiplatform dataset: hyperspectral image classification of ROSIS and Jilin-1 GP01 imagery
T KAVZOĞLU, EÖ Yilmaz - Turkish Journal of Electrical …, 2022 - journals.tubitak.gov.tr
Modern hyperspectral sensors provide a huge volume of data at spectral and spatial
domains with high redundancy, which requires robust methods for analysis. In this study, 2D …
domains with high redundancy, which requires robust methods for analysis. In this study, 2D …
Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification
Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles
(UAV) or satellite imagery has become increasingly popular for crop classification …
(UAV) or satellite imagery has become increasingly popular for crop classification …