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 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 …
Dimensionality reduction and classification of hyperspectral image via multistructure unified discriminative embedding
Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …
hyperspectral image (HSI). However, the present graph-based methods just consider the …
Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images
Z Lv, F Wang, G Cui, JA Benediktsson… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Land cover change detection (LCCD) using remote sensing images (RSIs) plays an
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
Iterative training sample augmentation for enhancing land cover change detection performance with deep learning neural network
Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …
deep learning techniques with remote sensing images. However, labeling samples for …
Novel adaptive region spectral–spatial features for land cover classification with high spatial resolution remotely sensed imagery
Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …
Building change detection for VHR remote sensing images via local–global pyramid network and cross-task transfer learning strategy
Building change detection (BCD) for very-high-spatial-resolution (VHR) remote sensing
images is very important and challenging in the field of remote sensing, as the building is …
images is very important and challenging in the field of remote sensing, as the building is …
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 …
Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient
GM Foody - Plos one, 2023 - journals.plos.org
The accuracy of a classification is fundamental to its interpretation, use and ultimately
decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the …
decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the …
A multidepth and multibranch network for hyperspectral target detection based on band selection
Deep learning (DL) has recently risen to prominence in hyperspectral target detection
(HTD). Nevertheless, how to tackle the extreme training sample imbalance together with …
(HTD). Nevertheless, how to tackle the extreme training sample imbalance together with …