Change detection on remote sensing images using dual-branch multilevel intertemporal network
Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by
the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed …
the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed …
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
In this paper, we propose a context-sensitive technique for unsupervised change detection
in multitemporal remote sensing images. The technique is based on fuzzy clustering …
in multitemporal remote sensing images. The technique is based on fuzzy clustering …
Spatiotemporal enhancement and interlevel fusion network for remote sensing images change detection
Remote sensing (RS) image change detection (CD) plays a crucial role in monitoring
surface dynamics; however, current deep learning (DL)-based CD methods still suffer from …
surface dynamics; however, current deep learning (DL)-based CD methods still suffer from …
Bifa: Remote sensing image change detection with bitemporal feature alignment
Despite the success of deep learning-based change detection (CD) methods, their existing
insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them …
insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them …
Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images
SM Vicente-Serrano, F Pérez-Cabello… - Remote sensing of …, 2008 - Elsevier
The homogeneity of time series of satellite images is crucial when studying abrupt or
gradual changes in vegetation cover via remote sensing data. Various sources of noise …
gradual changes in vegetation cover via remote sensing data. Various sources of noise …
An unsupervised domain adaptation approach for change detection and its application to deforestation mapping in tropical biomes
PJS Vega, GAOP da Costa, RQ Feitosa… - ISPRS Journal of …, 2021 - Elsevier
Abstract Changes in environmental conditions, geographical variability and different sensor
properties typically make it almost impossible to employ previously trained classifiers for …
properties typically make it almost impossible to employ previously trained classifiers for …
Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data
Wildfire is an important disturbance agent in Canada's boreal forest. Optical remotely
sensed imagery (eg, Landsat TM/ETM+), is well suited for capturing horizontally distributed …
sensed imagery (eg, Landsat TM/ETM+), is well suited for capturing horizontally distributed …
The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia
Wetlands provide multiple ecosystem services such as storing and regulating water flows
and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic …
and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic …
Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation
Discrete changes in forest abundance, distribution, and productivity are readily detectable
using a number of remotely sensed data sources; however, continuous changes such as …
using a number of remotely sensed data sources; however, continuous changes such as …
Recent advances on 2D and 3D change detection in urban environments from remote sensing data
K Karantzalos - Computational Approaches for Urban Environments, 2015 - Springer
Urban environments are dynamic and complex by nature, evolve over time, and constitute
the key elements for currently emerging environmental and engineering applications in …
the key elements for currently emerging environmental and engineering applications in …