How to use remote sensing images to distinguish two types of plants? (One is a shrub with coniferous leaves; the other is a tree with broad leaves)

Remote sensing images can provide important information for plant species identification, but require certain technology and data analysis. Here is how to distinguish two types of plants (shrubs and trees) using remote sensing images:

1. Understand the characteristics of the target plant: Before starting the analysis, you need to understand the typical characteristics and morphology of shrubs and trees. Shrubs are usually lower and have no obvious trunk, while trees are taller and have obvious trunks and branches.

2. Select appropriate remote sensing images: Select remote sensing images containing target plants, such as satellite images or high-resolution aerial images. These images can provide important information about the plant's form, color, and texture.

3. Data preprocessing: Before data analysis, remote sensing images need to be preprocessed. This includes adjusting the contrast and brightness of the image, filtering to remove noise, adjusting color balance, and more.

4. Feature extraction: Extract features related to plant species from the preprocessed image. These features can include color, texture, shape, height, etc. Computer vision and image processing techniques can be used to extract these features.

5. Classifier design: Use the extracted features and images with known labels to train a classifier to distinguish shrubs and trees. Classifiers can be designed using machine learning algorithms, such as support vector machines (SVM), random forests, or neural networks, etc.

6. Model evaluation and result visualization: Use the test data set to evaluate the performance of the classifier. The visualization results can map the classification results back to the original image to show the accuracy of the classifier.

It should be noted that this method may be affected by factors such as image quality, resolution, and background noise. Therefore, before classification, sufficient preprocessing and feature extraction are required, and a suitable classifier algorithm is selected.