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https://github.com/yzhq97/cnn-registration
An image registration method using convolutional neural network features.
https://github.com/yzhq97/cnn-registration
convolutional-neural-networks image-registration
Last synced: about 1 month ago
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An image registration method using convolutional neural network features.
- Host: GitHub
- URL: https://github.com/yzhq97/cnn-registration
- Owner: yzhq97
- License: mit
- Created: 2018-02-11T09:12:45.000Z (over 6 years ago)
- Default Branch: publish
- Last Pushed: 2020-12-28T10:00:45.000Z (over 3 years ago)
- Last Synced: 2024-02-09T16:07:56.620Z (5 months ago)
- Topics: convolutional-neural-networks, image-registration
- Language: Python
- Homepage:
- Size: 35.6 MB
- Stars: 417
- Watchers: 12
- Forks: 137
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - cnn-registration - An image registration method using convolutional neural network features. (Python)
- awesome-image-alignment-and-stitching - 2018Acce
- repo-8825-awesome-image-alignment-and-stitching - 2018Acce
README
# cnn-registration
A image registration method using convolutional neural network features written in Python2, Tensorflow API r1.5.0.
# Introduction
Registration of multi-temporal remote sensing images has been widely applied in military and
civilian fields, such as ground target identification, urban development assessment and geographic change
assessment. Ground surface change challenges feature point detection in amount and quality, which is
a common dilemma faced by feature based registration algorithms. Under severe appearance variation,
detected feature points may contain a large proportion of outliers, whereas inliers may be inadequate
and unevenly distributed. This work presents a convolutional neural network (CNN) feature based multitemporal
remote sensing image registration method with two key contributions: (i) we use a CNN to generate
robust multi-scale feature descriptors; (ii) we design a gradually increasing selection of inliers to improve the
robustness of feature points registration. Extensive experiments on feature matching and image registration
are performed over a multi-temporal satellite image dataset and a multi-temporal unmanned aerial vehicle
(UAV) image dataset. Our method outperforms four state-of-the-art methods in most scenarios.
The paper "Multi-temporal Remote Sensing Image Registration Using Deep Convolutional Features" has been published on IEEE Access. See https://ieeexplore.ieee.org/document/8404075/.
citation information:
```
@article{
author={Z. Yang and T. Dan and Y. Yang},
journal={IEEE Access},
title={Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features},
year={2018},
volume={6},
pages={38544-38555},
doi={10.1109/ACCESS.2018.2853100},
ISSN={2169-3536}
}
```
# Requirements
* numpy
* scipy
* opencv-python
* matplotlib
* tensorflow (with or without gpu)
* lap
To install all the requirements run
```
pip install -r requirements.txt
```
Prior to doing so, in some Linux distributions, you may need to install software packages such as the following:
* pip
* python2 development package
* python-setup
and you may need to do "pip install wheel".
Pretrained VGG16 parameters file `vgg16partial.npy` is available at `https://drive.google.com/file/d/1o1xjU9F58x83iR91LoFjLOlBdLN3bPnm/view?usp=sharing`.
Please download and put it under the `src/` directory.
# Usage
see src/demo.py
```python
import Registration
from utils.utils import *
import cv2
# load images
IX = cv2.imread(IX_path)
IY = cv2.imread(IY_path)
#initialize
reg = Registration.CNN()
#register
X, Y, Z = reg.register(IX, IY)
#generate regsitered image using TPS
registered = tps_warp(Y, Z, IY, IX.shape)
```