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Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks

Official implementation for Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters 2019

Citation

Please cite our project if it is helpful for your research

A. Bahri, S. G. Majelan, S. Mohammadi, M. Noori and K. Mohammadi, "Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks," in IEEE Geoscience and Remote Sensing Letters.

UCMerced land-use dataset

<img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/d5a84f12-91af-4b3d-8096-2d4a5e641ecc-usa.png"> 21 class UC Merced land-use Dataset (RGB)

Architecture

<img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/08191186-61c0-4298-80f0-85825f8ba2b4-udsd.png"> Our Architecture

Dependencies

1 Nvidia GPU (4h training on Titan Xp)
Python3
tensorflow 1.15
numpy 1.17.5
keras 2.2.5

Download original datasets

AID (Aerial Image Dataset)
Download (https://drive.google.com/file/d/1D8gvnEvzbyNlZHLLD3zqLGiUaxgqp0yN/view?usp=sharing)
NWPU-RESISC45 (Northwestern Polytechnic University Remote Sensing Image Scene Classification 45)
Download (https://drive.google.com/file/d/1eOMQ7zF19KRvjxZqVESMYzAd9X6gZfar/view?usp=sharing)
UC Merced land-use
Download (https://drive.google.com/file/d/1rzNVDsRn3JcVNnAYCI_eyd43ZoU_5vuq/view?usp=sharing)
WHU-RS19
Download (https://drive.google.com/file/d/1KuTwHU9Yumswrp9K1_FK0dlMN8QRjN-y/view?usp=sharing)

Use ready datasets (splited to train and valid parts)

AID (Aerial Image Dataset) (train: 50%, valid: 50%)
Address (https://drive.google.com/drive/folders/10U9jzimYUtD9iGn9am3cricgEpbfNuJV?usp=sharing)
AID (Aerial Image Dataset) (train: 70%, valid: 30%)
Address (https://drive.google.com/drive/folders/11hTqDCVB-hoEDWMTMIeGbPyAytKDQ8IA?usp=sharing)
NWPU-RESISC45 (Northwestern Polytechnic University Remote Sensing Image Scene Classification 45) (train: 20%, valid: 80%)
Address (https://drive.google.com/drive/folders/1X2oTWq8hJ-1Miy1mJyM4SD3l7SuZgN95?usp=sharing)
NWPU-RESISC45 (Northwestern Polytechnic University Remote Sensing Image Scene Classification 45) (train: 30%, valid: 70%)
Address (https://drive.google.com/drive/folders/1XJinSCqe8mLUcmj4KzW9nWzM8zhnW_g4?usp=sharing)
UC Merced land-use (train: 80%, valid: 20%)
Address (https://drive.google.com/drive/folders/15JMhL7peTdO8DZhyheYrkKbCUuFabeGT?usp=sharing)

Trained models

Download trained model on AID dataset (train: 70% , valid: 30%) with accuracy score: 98.10 (https://drive.google.com/file/d/1-2sb1gBU9oYN4SF-iZ4Xab1mwVnmk0AD/view?usp=sharing)
Download trained model on AID dataset (train: 50% , valid: 50%) with accuracy score: 97.08 (https://drive.google.com/file/d/1-1fHZODRLKUvRwlCBHLVCMo4e7E-32HX/view?usp=sharing)
Download trained model on UC Merced land-use dataset (train: 80% , valid: 20%) with accuracy score: 99.52 (https://drive.google.com/file/d/1-20x38XGckZCNM-wsV7Gvpif4jaCVRQN/view?usp=sharing)
Download trained model on NWPU-RESISC45 dataset (train: 20% , valid: 80%) with accuracy score: 93.56 (https://drive.google.com/file/d/1-Ey8NkAa0HksmSrw7opIB1oATGD5_jH5/view?usp=sharing)
Download trained model on NWPU-RESISC45 dataset (train: 30% , valid: 70%) with accuracy score: 94.44 (https://drive.google.com/file/d/1hYcdtJHrviuDLGYwoodzJC9b23FnUj2q/view?usp=sharing)

Project layout (recommended)

Remote_Sensing_Image_Classification/
├── checkpoint
├── data
│   ├── AID (train:70%, valid:30%)
│   ├── AID (train:50%, valid:50%)
│   ├── UCMerced (train:80%, valid:20%)
│   ├── NWPU-RESISC45 (train:30%, valid:70%)
│   └── NWPU-RESISC45 (train:20%, valid:80%)
├── docs
└── trained_models
    ├── NasNet_Mobile_New_Loss3.02-0.9810(AID_70_30).h5
    ├── NasNet_Mobile_New_Loss3.19-0.9708(AID_50_50).h5
    ├── NasNet_Mobile_New_Loss3.117-0.9952(UCMerced_80_20).h5
    ├── NasNet_Mobile_New_Loss3_Dore_3.06-0.9356(NWPU_20_80).h5
    └── NasNet_Mobile_New_Loss3_94.43(NWPU_30_70).h5

Quick start to validate(using ready datasets)

Use ready dataset path (only valid part)
Download trained models and put into trained_models/ directory
Run python predict.py
Results will be shown.

Note: Configurations is in the config.py file.

Start to validate (using original datasets)

Download original dataset and put into data/ directory.
Unzip dataset
Run python divide_dataset.py to split dataset to train and valid folder
Download trained models and put into trained_models/ directory
Run python predict.py
Results will be shown.

Note: Configurations is in the config.py file.

Quick start to Training (using ready datasets)

Use ready dataset path
Run python train.py
All Models will be saved into checkpoint/ direcory

Note: Configurations is in the config.py file.

Start to Training (using original datasets)

Download original dataset and put into data/ directory
Unzip dataset
Run python divide_dataset.py to split dataset to train and valid folder
Run python train.py
All Models will be saved into checkpoint/ direcory

Note: Configurations is in the config.py file.

Quantitative and Qualitative results

<img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/Bootstrap Chart.PNG"> Bootstrap Chart for NWPU-RESISC45 Dataset     <img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/t_1.PNG"> OVERALL ACCURACY OF THE REFERENCE AND THE PROPOSED METHOD ON THE UC MERCED DATASET     <img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/t_2.PNG"> OVERALL ACCURACY OF THE REFERENCE AND THE PROPOSED METHOD ON THE AID DATASET (50% TRAINING, 50% TESTING)     <img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/t_3.PNG"> OVERALL ACCURACY OF THE REFERENCE AND THE PROPOSED METHOD ON THE AID DATASET (70% TRAINING, 30% TESTING)     <img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/t_4.PNG"> OVERALL ACCURACY OF THE REFERENCE AND THE PROPOSED METHOD ON THE NWPU-RESISC45 DATASET (20% TRAINING,80% TESTING)     <img src="https://github.com/AliBahri94/Remote-Sensing-Image-Classification/blob/master/docs/t_5.PNG"> OVERALL ACCURACY OF THE REFERENCE AND THE PROPOSED METHOD ON THE NWPU-RESISC45 DATASET (30% TRAINING,70% TESTING)