Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data
Q Sun, J Chao, W Lin, Z Xu, W Chen, N He - Remote Sensing, 2023 - mdpi.com
Few-shot semantic segmentation (FSS) is committed to segmenting new classes with only a
few labels. Generally, FSS assumes that base classes and novel classes belong to the same …
few labels. Generally, FSS assumes that base classes and novel classes belong to the same …
Forest Cover Change Monitoring Using Sub-Pixel Map** with Edge-Matching Correction
S ** of forest cover is performed on Sentinel-2 images, downscaling the …
Invasive plants detection and distribution patterns analysis through self-attention enhanced semantic segmentation in UAV imagery and Moran's index
The development of sustainable agriculture necessitates the rapid identification and efficient
removal of invasive plants. Traditionally, the investigation of invasive plants relies on manual …
removal of invasive plants. Traditionally, the investigation of invasive plants relies on manual …
Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation
C Kwenda, M Gwetu, JV Fonou-Dombeu - Journal of Imaging, 2024 - mdpi.com
Forests play a pivotal role in mitigating climate change as well as contributing to the socio-
economic activities of many countries. Therefore, it is of paramount importance to monitor …
economic activities of many countries. Therefore, it is of paramount importance to monitor …
A Hierarchic Method of Individual Tree Canopy Segmentation Combing UAV Image and LiDAR
R Wang, C Hu, J Han, X Hu, Y Zhao, Q Wang… - Arabian Journal for …, 2024 - Springer
The individual tree crown segmentation (ITCs) technology based on unmanned aerial
vehicle (UAV) remote sensing is an important means for remote forestry parameter …
vehicle (UAV) remote sensing is an important means for remote forestry parameter …
Forest Segmentation: Spatio-Temporal Ground Truth Labelling via Assisted Annotation
IM Jelas, MA Zulkifley… - 2024 IEEE 8th International …, 2024 - ieeexplore.ieee.org
Accurate ground truth annotation is essential for training and evaluating deep learning
models for remote sensing applications, particularly for tasks such as forest and non-forest …
models for remote sensing applications, particularly for tasks such as forest and non-forest …
Automated Ground Truth Annotation for Forest and Non-Forest Classification in Satellite Remote Sensing Images
IM Jelas, MA Zulkifley… - 2023 4th International …, 2023 - ieeexplore.ieee.org
Accurate ground truth annotation plays a vital role in training and evaluating deep learning
models for forest and non-forest classification tasks. This paper introduces a robust …
models for forest and non-forest classification tasks. This paper introduces a robust …
Automatic Identification of Forest Areas in the “Carolina” Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study
J Guapaz, JP Jervis, D Haro, J Padilla… - … Conference on Applied …, 2024 - Springer
This study explores the challenges of identifying forest areas in the “Carolina” Park in Quito,
Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support …
Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support …
A novel two-step framework for map** fraction of mulched film based on very high resolution satellite observation and deep learning
The fraction of mulched film is of great significance for evaluating the agricultural water-
saving effect and controlling environmental plastic pollution. Unfortunately, there is no work …
saving effect and controlling environmental plastic pollution. Unfortunately, there is no work …