diff --git a/utils/visualizer.py b/utils/visualizer.py
index f74f5a9..302fbfd 100644
--- a/utils/visualizer.py
+++ b/utils/visualizer.py
@@ -3,6 +3,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import logging
 from typing import Optional
+from pyproj import Transformer
 
 
 class FilterVisualizer:
@@ -17,6 +18,25 @@ class FilterVisualizer:
         """
         self.output_dir = output_dir
         self.logger = logging.getLogger('UAV_Preprocess.Visualizer')
+        # 创建坐标转换器
+        self.transformer = Transformer.from_crs(
+            "EPSG:4326",  # WGS84经纬度坐标系
+            "EPSG:32649",  # UTM49N
+            always_xy=True
+        )
+        
+    def _convert_to_utm(self, lon: pd.Series, lat: pd.Series) -> tuple:
+        """
+        将经纬度坐标转换为UTM坐标
+        
+        Args:
+            lon: 经度序列
+            lat: 纬度序列
+            
+        Returns:
+            tuple: (x坐标, y坐标)
+        """
+        return self.transformer.transform(lon, lat)
         
     def visualize_filter_step(self, 
                             current_points: pd.DataFrame,
@@ -38,34 +58,40 @@ class FilterVisualizer:
         filtered_files = set(previous_points['file']) - set(current_points['file'])
         filtered_points = previous_points[previous_points['file'].isin(filtered_files)]
         
+        # 转换坐标到UTM
+        current_x, current_y = self._convert_to_utm(current_points['lon'], current_points['lat'])
+        filtered_x, filtered_y = self._convert_to_utm(filtered_points['lon'], filtered_points['lat'])
+        
         # 创建图形
+        plt.rcParams['font.sans-serif']=['SimHei']#黑体
+        plt.rcParams['axes.unicode_minus'] = False
         plt.figure(figsize=(20, 16))
         
         # 绘制保留的点
-        plt.scatter(current_points['lon'], current_points['lat'],
-                   color='blue', label='Retained Points',
+        plt.scatter(current_x, current_y,
+                   color='blue', label='保留的点',
                    alpha=0.6, s=50)
         
         # 绘制被过滤的点
         if not filtered_points.empty:
-            plt.scatter(filtered_points['lon'], filtered_points['lat'],
-                       color='red', marker='x', label='Filtered Points',
+            plt.scatter(filtered_x, filtered_y,
+                       color='red', marker='x', label='过滤的点',
                        alpha=0.6, s=100)
         
         # 设置图形属性
-        plt.title(f"GPS Points After {step_name}\n"
-                 f"(Filtered: {len(filtered_points)}, Retained: {len(current_points)})",
+        plt.title(f"{step_name}后的GPS点\n"
+                 f"(过滤: {len(filtered_points)}, 保留: {len(current_points)})",
                  fontsize=14)
-        plt.xlabel("Longitude", fontsize=12)
-        plt.ylabel("Latitude", fontsize=12)
+        plt.xlabel("东向坐标 (米)", fontsize=12)
+        plt.ylabel("北向坐标 (米)", fontsize=12)
         plt.grid(True)
         
         # 添加统计信息
         stats_text = (
-            f"Original Points: {len(previous_points)}\n"
-            f"Filtered Points: {len(filtered_points)}\n"
-            f"Remaining Points: {len(current_points)}\n"
-            f"Filter Rate: {len(filtered_points)/len(previous_points)*100:.1f}%"
+            f"原始点数: {len(previous_points)}\n"
+            f"过滤点数: {len(filtered_points)}\n"
+            f"保留点数: {len(current_points)}\n"
+            f"过滤率: {len(filtered_points)/len(previous_points)*100:.1f}%"
         )
         plt.figtext(0.02, 0.02, stats_text, fontsize=10,
                    bbox=dict(facecolor='white', alpha=0.8))