Abstract
Being an important economic crop that contributes 35% of the total consumption of vegetable oil, remote sensing-based quantitative detection of oil palm trees has long been a key research direction for both agriculture and environmental purposes. While existing methods already demonstrate satisfactory effectiveness for small regions, performing the detection for a large region with satisfactory accuracy is still challenging. In this study, we proposed a two-stage convolutional neural network (TS-CNN)-based oil palm detection method using high-resolution satellite images (i.e. Quickbird) in a large-scale study area of Malaysia. The TS-CNN consists of one CNN for land cover classification and one CNN for object classification. The two CNNs were trained and optimized independently based on 20,000 samples collected through human interpretation. For the large-scale oil palm detection for an area of 55 km2, we proposed an effective workflow that consists of an overlapping partitioning method for large-scale image division, a multi-scale sliding window method for oil palm coordinate prediction, and a minimum distance filter method for post-processing. Our proposed approach achieves a much higher average F1-score of 94.99% in our study area compared with existing oil palm detection methods (87.95%, 81.80%, 80.61%, and 78.35% for single-stage CNN, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), respectively), and much fewer confusions with other vegetation and buildings in the whole image detection results.
Highlights
Oil palm is one of the most rapidly expanding crops in tropical regions due to its high economic value [1], especially in Malaysia and Indonesia, the two leading oil palm-producing countries
We proposed a two-stage CNN-based method for oil palm detection from a fact that the neighboring oil palm trees often have similar spatial distances while other types of vegetation are often distributed randomly, it will be much easier to identify the oil palms from other types of vegetation if we take the special pattern or texture of the oil palm plantation areas into consideration
We proposed a two-stage convolutional neural network-based oil palm
Summary
Oil palm is one of the most rapidly expanding crops in tropical regions due to its high economic value [1], especially in Malaysia and Indonesia, the two leading oil palm-producing countries. The palm oil produced from the oil palm trees can be widely used for many purposes, e.g., producing cooking oil, food additive, cosmetics, industrial lubricants, and biofuels, etc. Palm oil has become the world’s most consumed vegetable oil, making up 35% of the total consumption of vegetable oil [3]. As a result of the increasing demand of palm oil, a considerable amount of land (e.g., existing arable land and forests) has been replaced by oil palm plantation areas [4,5]. The expansion of oil palm plantation areas has caused serious environmental problems, e.g., deforestation, the reduction of biodiversity, and the loss of ecosystem functioning, etc. The economic benefits of the oil palm
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