Awesome
CDL-Segmentation
This Repository contains code (will be published after paper acceptance) and benchmark results for the paper:
Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study
Paper Link: https://ieeexplore.ieee.org/document/9441483 <br> Accepted at: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)
Segmentation Models
- UNet
- SegNet
- DeepLabv3+
Pre-requistes
Main packages required are:
- Keras
- Tensorflow 1.15.0
- Numpy
- Skimage
- Matplotlib
Datasets
We used Google Earth Engine to generate the Dataset from Crop Land Data Layer provided by United States Department of Agriculture (USDA) and National Agricultural Statistics Service (NASS). Our Study area for analyzing performance of segmentation models is comprised of 9 different counties of state of Nebraska, United States of America. We emphasize on Classifying two categories, thus two different datasets are:- (1) Cultivated and Non-Cultivated Land Cover (2) Out of Cultivated area we classify the crop type i.e. Corn, Soya Bean, Winter Wheat, Alfalfa Hay and Others.
Visual Results
Top Images are Landsat8 image, Ground-Truth and Predicted Results for cultivated area respectively (Black pixels represent non-cultivated and white pixels are cultivated area, whereas Bottom Images are of Landsat8, Ground-Truth and classified Crops (Yellow as Corn, Green as Soyabean, orange as Winter Wheat, Pink as Alfalfa and Black as Other crops).
Cultivated/Non-Cultivated Land Cover
Out of Cultivated area classified Crop Types
Quantitative Results
Dataset: LandCover Classification (Cultivated/Non-Cultivated Land area)
UNet
| Results | Values |
|---|---|
| Accuracy | 89.5 |
| Dice co-efficient | 89.2 |
SegNet
| Results | Values |
|---|---|
| Accuracy | 74.69 |
| Dice co-efficient | 73.43 |
DeepLabv3
| Results | Values |
|---|---|
| Accuracy | 89.13 |
| Dice co-efficient | 88.6 |
Dataset Crop type Classification
UNet
| Results | Values |
|---|---|
| Accuracy | 67.3 |
| Dice co-efficient | 57.1 |
SegNet
| Results | Values |
|---|---|
| Accuracy | 49.5 |
| Dice co-efficient | 37.0 |
DeepLabv3
| Results | Values |
|---|---|
| Accuracy | 69.7 |
| Dice co-efficient | 62.02 |
Performance Graphs
UNet
Part 1: Cultivated/Non-Cultivated LandCover Classification
Loss: UNet vs SegNet vs DeepLabv3
Accuracy: UNet vs SegNet vs DeepLabv3
Dice Similarity Co-efficient: UNet vs SegNet vs DeepLabv3
Part 2: Crop Type Classification
Loss: UNet vs SegNet vs DeepLabv3
Accuracy: UNet vs SegNet vs DeepLabv3
Dice Similarity Co-efficient: UNet vs SegNet vs DeepLabv3
Maintainer
Asim Hameed Khan (asimhameed.cs@gmail.com)