Change detection for remote sensing images

21 papers with code • 2 benchmarks • 4 datasets

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Use these libraries to find Change detection for remote sensing images models and implementations

Most implemented papers

Fully Convolutional Siamese Networks for Change Detection

likyoo/open-cd 19 Oct 2018

This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.

Lake Ice Monitoring with Webcams and Crowd-Sourced Images

czarmanu/deeplab-lakeice-webcams 18 Feb 2020

On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.

DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images

lehaifeng/DASNet 7 Mar 2020

However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

Z-Zheng/ChangeStar ICCV 2021

For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images.

An Empirical Study of Remote Sensing Pretraining

vitae-transformer/vitae-transformer-remote-sensing 6 Apr 2022

To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.

TINYCD: A (Not So) Deep Learning Model For Change Detection

andreacodegoni/tiny_model_4_cd 26 Jul 2022

Despite being from 13 to 140 times smaller than the compared change detection models, and exposing at least a third of the computational complexity, our model outperforms the current state-of-the-art models by at least $1\%$ on both F1 score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD dataset.

Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images

Bobholamovic/CDLab IEEE Transactions on Image Processing 2022

In view of these issues, we propose a more explicit and sophisticated modeling of time and accordingly establish a pair-to-video change detection (P2V-CD) framework.


czarmanu/tiramisu_keras ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2018

Continuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system.

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

daifeng2016/End-to-end-CD-for-VHR-satellite-image 10 Jun 2019

To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets.

Lake Ice Detection from Sentinel-1 SAR with Deep Learning

czarmanu/sentinel_lakeice 17 Feb 2020

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.