# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 3 Foundation Model-Enhanced Coastal Land Cover Recognition
### Image Gridding-Based Land Use Classification on Non-Water Areas
#### 3.4.2 Image-Gridding Land Cover Classification Strategy  
To fully recognize all possible forms of land cover, we propose an image gridding strategy to enhance the classification with a foundation model; the detailed workflow is displayed in Figure 3. First, varied portions of different land covers in an optical satellite image may considerably affect the land cover classification results. Thus, we need to adopt the classical strategy for classification, which involves dividing the whole image into multiple subregions and then classifying each subregion.  
Assuming an original image is \(\text{Img}\big{(}2,\text{x}/2^{\text{k}},y/2^{\text{k}}\big{)}^{\text{k}}\), x and y refer to the horizontal and vertical dimensions, s refers to the image's index, and k refers to the image gridding scale; the original image has s = 0 and k = 0.  
Step 1. Classifying \(\text{Img}\big{(}\text{s},\text{x}/2^{\text{k}},y/2^{\text{k}}\big{)}\) and selecting all promising land cover categories that have a classification score higher than \(\theta\), \(\theta=0.6\);  
Step 2. If the number of selected categories exceeds 1, skip to Step 3; otherwise, skip to Step 4.  
Step 3. Dividing the previous image region into four subregions: \(\text{Img}\big{(}1,\text{x}/2^{\text{k}+1},y/2^{\text{k}+1}\big{)}\), \(\text{Img}\big{(}2,\text{x}/2^{\text{k}+1},y/2^{\text{k}+1}\big{)}\), \(\text{Img}\big{(}3,\text{x}/2^{\text{k}+1},y/2^{\text{k}+1}\big{)}\), and \(\text{Img}\big{(}4,\text{x}/2^{\text{k}+1},y/2^{\text{k}+1}\big{)}\); for each sub-region, return to Step 1.  
Step 4. Combining the categories generated from the whole image and multiple sub-regions.  
In the example shown in Figure 3, we first perform multi-label land cover classification on the original image, \(\text{Img}(0,\text{x},\text{y})\). If the amount of promising land cover form equals 1, it becomes the land use category assigned to the image \(\text{Img}(0,\text{x},\text{y})\); otherwise, we divide the original image into four parts: \(\text{Img}(1,\text{x}/2,\text{y}/2)\), \(\text{Img}(2,\text{x}/2,\text{y}/2)\), \(\text{Img}(3,\text{x}/2,\text{y}/2)\), and \(\text{Img}(4,\text{x}/2,\text{y}/2)\). For the second part, \(\text{Img}(2,\text{x}/2,\text{y}/2)\), we perform multi-label land cover classification and select all promising land cover forms. By analog, if the amount of the promising land cover form equals 1, it becomes the land use category assigned to subregion \(\text{Img}(2,\text{x}/2,\text{y}/2)\); otherwise, we further divide the subregion \(\text{Img}(2,\text{x}/2,\text{y}/2)\) into four subregions: \(\text{Img}(1,\text{x}/4,\text{y}/4)\), \(\text{Img}(2,\text{x}/4,\text{y}/4)\), \(\text{Img}(3,\text{x}/4,\text{y}/4)\), and \(\text{Img}(4,\text{x}/4,\text{y}/4)\). Then, we classify the second subregion, \(\text{Img}(2,\text{x}/4,\text{y}/4)\), into various promising land cover forms. If the amount of promising land cover category equals 1, it becomes the land use category assigned to subregion \(\text{Img}(4,\text{x}/4,\text{y}/4)\); otherwise, we divide this subregion into four subregions, and so on. In the end, we compile all promising forms into a coastal land cover feature vector. The details for processing this feature vector are presented in the following section.  
Figure 3: Illustration on the workflow of image gridding-based land cover classification.