## 4 Experiments  
### Study Area and Dataset  
In this research, the proposed method was applied to detect oil palm in remote sensing images from three different regions. As one of the major tropical cash crops in the world, the detection and counting of oil palm is of great significance to both economy and ecology.  
As shown in Figure 2, we obtained two high-resolution satellite images (i.e., images A and B) in Peninsula Malaysia [44]. These two images were acquired at different times and places with different equipment, and therefore, they differed significantly in environmental conditions and resolution. Another remote sensing image (image C) was taken by a UAV in South Kalimantan, Indonesia [57]. Table 2 shows the elaborate information of the three images.  
In images A and B, there were four kinds of samples: background, oil palm, other vegetation, and the impervious background. The training datasets were collected from four regions of these two images, respectively. To evaluate the performance of our proposed method, we chose another representative region in images A and B as the validation datasets and compared the detected results with the ground truth collected by manual annotation. For images from training and validation regions, a bilinear interpolation was first applied to resize them to 2400 \(\times\) 2400 pixels, and then the enlarged images were cropped randomly to 500 \(\times\) 500 pixels. Finally, we obtained 4718 samples in image A and 3782 samples in image B.  
In image C, in addition to the tree crown, there were also rivers, buildings, and other vegetation. The training datasets were from one region, and the validation datasets were from the other one. Since the images were originally collected for growing status observation by [PERSON] et al. [57], there were more varieties in crown size and appearance compared with images A and B, such as small palm and yellowed palm. We firstly unified their labels into oil palm and then split the training region into 3148 images and the validation region into 851 images with 1024 \(\times\) 1024 pixels.  
Figure 2 shows the location of our study area and the examples of the training region from three locations. We can easily observe that the tree crowns distributed intensively and had a low category diversity. Table 3 shows information on the three datasets.  
Figure 2: The location of our study area and training examples used in this paper. We obtained images from three sets (i.e., (**A–C**)), the images randomly selected from the training region are arranged at the top, bottom, and right of the study area image, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Image A** & **Image B** & **Image C** \\ \hline Source & Quick Bird & Google Earth & Skywalker X8, Sony \\  & & & a6000 \\ Longitude & 100.7772 E & 103.5991 E & 115.0375 E \\  & 4.1920 N & 1.5967 N & 3.1306 S \\  & & RGB & RGB,NIR & RGB \\ Acquisition & 2015.12.21 & 2006.11.21 & – \\ Resolution & 0.3 m & 0.6 m & 0.1 m \\ Image size & 10,496 \(\times\) 10,240 & 12,188 \(\times\) 12,576 & 40,000 \(\times\) 20,000 \\ Area & 9.67 km\({}^{2}\) & 55.18 km\({}^{2}\) & 8 km\({}^{2}\) \\ Number of trees & 91,357 & 291,827 & 86,994 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The necessary information about image A, image B and image C.