Deep learning-based change detection in remote sensing images: A review
Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …
development of remote sensing (RS) technology. These images significantly enhance the …
Land cover change detection with heterogeneous remote sensing images: Review, progress, and perspective
With the fast development of remote sensing platforms and sensors technology, change
detection with heterogeneous remote sensing images (Hete-CD) has become an attractive …
detection with heterogeneous remote sensing images (Hete-CD) has become an attractive …
Change detection methods for remote sensing in the last decade: A comprehensive review
Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …
detect and analyze changes occurring in the same geographical area over time, which has …
Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
In remote sensing images, change detection (CD) is required in many applications, such as:
resource management, urban expansion research, land management, and disaster …
resource management, urban expansion research, land management, and disaster …
Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review
Over the last few years, Deep learning (DL) approaches have been shown to outperform
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …
Novel piecewise distance based on adaptive region key-points extraction for LCCD with VHR remote-sensing images
Land cover change detection (LCCD) with very high-resolution remote-sensing images
(VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes …
(VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes …
Learning multiscale temporal–spatial–spectral features via a multipath convolutional LSTM neural network for change detection with hyperspectral images
Change detection (CD) with hyperspectral images (HSIs) can be effectively performed using
deep learning networks (DLNs) by taking advantage of HSIs for their abundant spectral and …
deep learning networks (DLNs) by taking advantage of HSIs for their abundant spectral and …
[HTML][HTML] The use of artificial intelligence and satellite remote sensing in land cover change detection: Review and perspectives
Z Gu, M Zeng - Sustainability, 2024 - mdpi.com
The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover
Change Detection (LCCD) has gained increasing significance in scientific discovery and …
Change Detection (LCCD) has gained increasing significance in scientific discovery and …
Change detection of multisource remote sensing images: a review
Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring,
territorial planning, and disaster assessment. With the abundance of data collected by …
territorial planning, and disaster assessment. With the abundance of data collected by …
[HTML][HTML] Quantitative assessment of Land use/land cover changes in a develo** region using machine learning algorithms: A case study in the Kurdistan Region …
The identification of land use/land cover (LULC) changes is important for monitoring,
evaluating, and preserving natural resources. In the Kurdistan region, the utilization of …
evaluating, and preserving natural resources. In the Kurdistan region, the utilization of …