Water-Level model is an effective method in density-based classification. We use biased sampling, local similarity and popularity as preprocessing, and employ a merging operation in the water-level model for classification. Biased sampling is to get some information about the global structure. Similarity and local density are mainly used to understand the local structure. In biased sampling, images are divided into many l x l patches and a sample pixel is selected from each patch. Similarity at a point p, denoted by sim(p), measures the change of gray level between point p and its neighborhood N(p). Besides using biased sampling to combine spectral and spatial information, we use similarity and local popularity in selecting sample points. A sample point is chosen based on the minimum value of sim(p) + [1-P(p)] after normalization. The selected pixel is a better representative, especially near the border of an object. To make it more effective, one has to deal with small spikes and bumps. To get rid of the small spikes, we establish a threshold |[f(P1)-f(P2)]*(P1-P2)| > c*l*l , where c is a constant, P1 is a local maximum point to be tested and P2 is the nearest local minimum from P1. The condition is only related to the size of the patches l*l. The merging operation we include in the model makes the threshold constant less sensitive in the process. DBScan is combined with the enhanced water level model to reduce noise and to get connected components. Preliminary experiments have been conducted using the proposed methods and the results are promising.