reachsumit / Deep Unet For Satellite Image Segmentation
Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet
Stars: ✭ 227
Programming Languages
Projects that are alternatives of or similar to Deep Unet For Satellite Image Segmentation
Keras Unet
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
Stars: ✭ 196 (-13.66%)
Mutual labels: deep-neural-networks, image-segmentation, keras-tensorflow, unet
Brain-MRI-Segmentation
Smart India Hackathon 2019 project given by the Department of Atomic Energy
Stars: ✭ 29 (-87.22%)
Mutual labels: image-segmentation, unet, keras-tensorflow
Segmentation models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Stars: ✭ 3,575 (+1474.89%)
Mutual labels: image-segmentation, keras-tensorflow, unet
Tf.keras Commonly Used Models
基于Tensorflow的常用模型，包括分类分割、新型激活、卷积模块，可在Tensorflow2.X下运行。
Stars: ✭ 115 (-49.34%)
Mutual labels: image-segmentation, unet
Multiclass Semantic Segmentation Camvid
Tensorflow 2 implementation of complete pipeline for multiclass image semantic segmentation using UNet, SegNet and FCN32 architectures on Cambridge-driving Labeled Video Database (CamVid) dataset.
Stars: ✭ 67 (-70.48%)
Mutual labels: image-segmentation, unet
Paddleseg
End-to-end image segmentation kit based on PaddlePaddle.
Stars: ✭ 1,244 (+448.02%)
Mutual labels: image-segmentation, unet
Segmentationcpp
A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
Stars: ✭ 49 (-78.41%)
Mutual labels: image-segmentation, unet
Semantic Segmentation Of Remote Sensing Images
遥感图像的语义分割，基于深度学习，在Tensorflow框架下，利用TF.Keras，运行环境TF2.0+
Stars: ✭ 125 (-44.93%)
Mutual labels: image-segmentation, unet
Open Solution Salt Identification
Open solution to the TGS Salt Identification Challenge
Stars: ✭ 124 (-45.37%)
Mutual labels: image-segmentation, unet
Pytorch Unet
Tunable U-Net implementation in PyTorch
Stars: ✭ 224 (-1.32%)
Mutual labels: deep-neural-networks, unet
Livianet
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
Stars: ✭ 143 (-37%)
Mutual labels: deep-neural-networks, image-segmentation
Bidaf Keras
Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2
Stars: ✭ 60 (-73.57%)
Mutual labels: deep-neural-networks, keras-tensorflow
Unet 3d
3D Unet Equipped with Advanced Deep Learning Methods
Stars: ✭ 57 (-74.89%)
Mutual labels: deep-neural-networks, unet
Har Keras Cnn
Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras
Stars: ✭ 97 (-57.27%)
Mutual labels: deep-neural-networks, keras-tensorflow
Unet Tensorflow
Tensorflow implement of U-Net
Stars: ✭ 50 (-77.97%)
Mutual labels: image-segmentation, unet
Hyperdensenet
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
Stars: ✭ 124 (-45.37%)
Mutual labels: deep-neural-networks, image-segmentation
Paddlex
PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）
Stars: ✭ 3,399 (+1397.36%)
Mutual labels: deep-neural-networks, unet
P2pala
Page to PAGE Layout Analysis Tool
Stars: ✭ 147 (-35.24%)
Mutual labels: deep-neural-networks, image-segmentation
Tf Keras Surgeon
Pruning and other network surgery for trained TF.Keras models.
Stars: ✭ 25 (-88.99%)
Mutual labels: deep-neural-networks, keras-tensorflow
Densedepth
High Quality Monocular Depth Estimation via Transfer Learning
Stars: ✭ 963 (+324.23%)
Mutual labels: deep-neural-networks, keras-tensorflow
Deep UNet for satellite image segmentation
!
About this project
This is a Keras based implementation of a deep UNet that performs satellite image segmentation.
Dataset
- The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset.
- Train collection contains few tiff files for each of the 24 locations.
- Every location has an 8-channel image containing spectral information of several wavelength channels (red, red edge, coastal, blue, green, yellow, near-IR1 and near-IR2). These files are located in data/mband/ directory.
- Also available are correctly segmented images of each training location, called mask. These files contain information about 5 different classes: buildings, roads, trees, crops and water (note that original Kaggle contest had 10 classes).
- Resolution for satellite images s 16-bit. However, mask-files are 8-bit.
Implementation
- Deep Unet architecture is employed to perform segmentation.
- Image augmentation is used for input images to significantly increases train data.
- Image augmentation is also done while testing, mean results are exported to result.tif image.
Note: Training for this model was done on a Tesla P100-PCIE-16GB GPU.
Prediction Example
Network architecture
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].