1. Understand the characteristics of target plants: Before analysis, it is necessary to understand the typical characteristics and morphology of shrubs and trees. Shrubs are usually very low and have no obvious trunks, while trees are very tall and have obvious trunks and branches.
2. Select the appropriate remote sensing image: Select the remote sensing image containing the target plant, such as satellite image or high-resolution aerial image. These images can provide important information such as plant morphology, 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 the color balance and so on.
4. Feature extraction: extract features related to plant species from the preprocessed image. These features can include color, texture, shape and height. 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 the classifier to distinguish shrubs from trees. Machine learning algorithm can be used to design classifiers, such as support vector machine (SVM), random forest or neural network.
6. Model evaluation and result visualization: Use test data set to evaluate the performance of classifier. 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 image quality, resolution and background noise. Therefore, before classification, it is necessary to carry out sufficient preprocessing and feature extraction, and select the appropriate classifier algorithm.