Patch-based CNN for Drone-based Bamboo Forest Image Classification
Chinsu Lin1, Wenzhi Liao2, Satoshi Tatsuhara3 , Sian-En Ma1 , Chien-Ju Huang1
1: Department of Forestry and Natural Forestry, National Chiayi University, TAIWAN. E-mail: chinsu@mail.ncyu.edu.tw
2: Flanders Make and Ghent University, BELGIUM. E-mail: wenzhi.liao@ugent.be
3: Department of Forest Science, Graduate School of Agriculture and Life Science, The University of Tokyo, JAPAN. E-mail: tatsu@fr.a.u-Tokyo.ac.jp

Abstract
Bamboo growth is closely related to temperature (better grown in high-temperature and humid environments in Asia, includingTaiwan) and has great potential for increasing carbon sequestration as it is well managed. However, the fast expansion of unmanaged
bamboo plantations can invade forests, inducing forest fragmentation and biodiversity loss. Therefore, classifying the bamboo forest
images and tracking their changes have impacts on environmental monitoring. This paper proposes a patch-based convolutional neuralnetwork (CNN) for very high-resolution bamboo forest remote sensing image classification. Experimental results from drone remotesensing images show that the proposed patch-based CNN can improve classification accuracy by more than 20% over classical objectbased classification.

1. Study Framework

3. From Pixel- to Batch-based Features for CNN

Objective: build models to predict and analyze which environmental factors (e.g.,temperature, elevation, soil properties) help to drive tree species distributions,offering some guidance on species sensitivity to environmental changes• Patch: take a pixel and its NxN neighboring pixels within a sliding window (byreflection padding to increase the edge pixels)• A remote sensing image with a size of 100x100 pixels will have 10000 patches• Treat a patch as an input image for CNN• Classify 100x100 pixels equals to classify 10000 patches

4. Experimental Design & Results

2. Conceptual Model••

Our goal is to classify each pixel to a specific class and comparing the
proposed approach’s performance with random forest classifier.
A unmanaged bamboo forest near rural-agricultural area is used.

LULC

Entire Image

Forest type

CoarseStandMedium

Tree

Fine

Image level

Object level

A: CNN uses kernels to learn and extract relevant features fromimage and classify this image to a specific class.

Study site: A outlook of study site from UAV as well as close-lookbamboo forest from the air and the ground.
True color image

Ground truth

Results

Pixel Level

Leaf

B: Meaningful hierarchical image object levels in high-resolution remotely sensed data
• Pixel-based: treat features of each pixel as the input• Object-based: first segment the image into several groups, then extract features ofeach group as the input

Left: SLIC segmented areas, Center: Ground truth for evaluation; Right: Pixel-based RF map

Left: Patch-based RF map; Right: Loss and accuracy curves for Patch-based CNN approach
Species

C: Patch-based CNN model
Acknowledgment: The authors would like to acknowledge the support provided by MOST 112-2119M-415-001 funded by the Ministry of Science and Technology, Taiwan, ROC.

BambooHonduran Mahogany
LycheeBananaMango
GrassBroadleaf
Coconut Palm
OthersMacro avg

Per-pixel
classification (RF)
0.59
0.16
0.19
0.26
0.19
0.52
0.31
0.30
0.98
0.39

Table: Accuracy metricObject-basedclassification (SLIC-RF)
0.75
0.44
0.64
0.70
0.62
0.83
0.70
0.75
0.98
0.71

Patch-based CNN
with a patch size of 15x15 (2D-CNN)
0.97
0.99
0.99
0.99
0.95
0.99
1.00
0.99
1.00
0.99