diff --git a/src/utils/find_severe_glint_area.py b/src/utils/find_severe_glint_area.py
index 0c28381..867d2b8 100644
--- a/src/utils/find_severe_glint_area.py
+++ b/src/utils/find_severe_glint_area.py
@@ -559,8 +559,234 @@ def create_water_mask_from_shp(shp_file, reference_raster):
         raise
 
 
+# ============================================================
+# ★ v3 两趟式（Two-Pass Chunking）耀斑检测
+#
+# 旧实现把"单波段影像 + 水体掩膜"整幅读入内存后做全局统计与逐像素检测，
+# 在 34914x19177（6.7 亿像素）影像上内存峰值可达 5~10 GB。
+# 这里改为：
+#   第一趟：按 block 抽样收集有效样本 → 求全局标量
+#           （zscore: mean/std；percentile: 分位；iqr: Q1/Q3；
+#            otsu: 2%/98% 拉伸分位 + 256-bin 直方图上的 Otsu 阈值）
+#   第二趟：按 block 读取 → 逐块应用阈值 → WriteArray 写 uint8 掩膜。
+# 全程每块只驻留 block_size² 数据，内存恒定。
+#
+# 限制说明（不影响默认 GUI 主路径）：
+#   - 'adaptive' 是整幅邻域算子（局部窗口百分位），仍需整幅数组；
+#   - max_area（连通域面积过滤）/ buffer_size（岸线缓冲）是全局结构后处理，
+#     同样需整幅数组。
+#   启用这些选项时仍走旧全幅路径，大影像上会告警。
+# ============================================================
+
+def _otsu_threshold_from_hist(hist):
+    """由 256-bin 直方图求 Otsu 阈值（0..255），复刻原 otsu() 的类间方差最大准则。"""
+    total = float(hist.sum())
+    if total <= 0:
+        return 0.0
+    vals = np.arange(256, dtype=np.float64)
+    sum_all = float((hist * vals).sum())
+    w_b = 0.0
+    sum_b = 0.0
+    max_between = -1.0
+    thr = 0.0
+    for t in range(256):
+        w_b += float(hist[t])
+        if w_b == 0:
+            continue
+        w_f = total - w_b
+        if w_f == 0:
+            break
+        sum_b += t * float(hist[t])
+        m_b = sum_b / w_b
+        m_f = (sum_all - sum_b) / w_f
+        between = w_b * w_f * (m_b - m_f) ** 2
+        if between > max_between:
+            max_between = between
+            thr = float(t)
+    return thr
+
+
+def _remove_raster_output(path):
+    """移除已有栅格输出（兼容 GTiff / ENVI .dat+.hdr）。"""
+    base = os.path.splitext(path)[0]
+    for c in dict.fromkeys([path, path + '.hdr', base + '.hdr', base + '.dat']):
+        if c and os.path.exists(c):
+            os.remove(c)
+
+
+def _detect_glint_two_pass(img_path, mask_arg, out_path, bands_1based, weights,
+                           method, z_threshold, percentile_value, iqr_multiplier,
+                           sample_every=None, block_size=2048):
+    """两趟分块耀斑检测并写出 uint8 掩膜（method ∈ zscore/percentile/iqr/otsu）。
+
+    Args:
+        img_path: 输入影像路径
+        mask_arg: 水体掩膜路径（栅格/.shp）；None/空串 → 全图皆为水域
+        out_path: 输出掩膜路径（.dat/.bil/.bsq→ENVI；.tif/.tiff→GTiff）
+        bands_1based: 参与检测的波段号列表（1-based，多波段时加权融合）
+        weights: 波段权重（None→等权）
+        method: 'zscore' | 'percentile' | 'iqr' | 'otsu'
+        z_threshold / percentile_value / iqr_multiplier: 方法参数
+    """
+    ds = gdal.Open(img_path)
+    if ds is None:
+        raise ValueError(f"无法打开影像文件: {img_path}")
+    cols, rows = ds.RasterXSize, ds.RasterYSize
+    geo, proj = ds.GetGeoTransform(), ds.GetProjection()
+
+    # 抽样步长自适应：目标全局样本量 ~20 万，兼顾统计代表性与内存
+    if sample_every is None:
+        px = max(1, cols * rows)
+        sample_every = min(256, max(1, int((px / 200000.0) ** 0.5)))
+    band_objs = [ds.GetRasterBand(b) for b in bands_1based]
+    nb = len(band_objs)
+    if nb == 0:
+        ds = None
+        raise ValueError("没有可用的检测波段")
+    if weights is None:
+        weights = [1.0] * nb
+    weights = [float(w) for w in weights]
+    total_w = sum(weights) or 1.0
+
+    # ---- 掩膜访问器：支持分块读取 ----
+    mask_band = None
+    wm_cleanup = None
+    if mask_arg is not None and str(mask_arg).strip() != "":
+        wm = str(mask_arg)
+        if wm.lower().endswith('.shp'):
+            wm_cleanup = out_path + '__glint_wm.tif'
+            from src.utils.extract_water_area import rasterize_shp
+            rasterize_shp(wm, wm_cleanup, img_path)
+            mds = gdal.Open(wm_cleanup)
+        else:
+            mds = gdal.Open(wm)
+        if mds is None:
+            raise ValueError(f"无法打开水域掩膜文件: {mask_arg}")
+        mask_band = mds.GetRasterBand(1)
+        if mask_band.XSize != cols or mask_band.YSize != rows:
+            raise ValueError("掩膜与影像尺寸不一致，两趟检测要求同网格")
+
+    def _read_fused(x, y, xs, ys):
+        if nb == 1:
+            return band_objs[0].ReadAsArray(x, y, xs, ys).astype(np.float32)
+        arr = None
+        for i, bo in enumerate(band_objs):
+            bi = bo.ReadAsArray(x, y, xs, ys).astype(np.float32) * (weights[i] / total_w)
+            arr = bi if arr is None else arr + bi
+        return arr
+
+    def _read_mask(x, y, xs, ys):
+        if mask_band is None:
+            return np.ones((ys, xs), dtype=bool)
+        return mask_band.ReadAsArray(x, y, xs, ys) > 0
+
+    def _create_output():
+        _remove_raster_output(out_path)
+        ext = os.path.splitext(out_path)[1].lower()
+        if ext in ('.tif', '.tiff'):
+            od = gdal.GetDriverByName('GTiff').Create(
+                out_path, cols, rows, 1, gdal.GDT_Byte, options=['COMPRESS=LZW'])
+        else:
+            od = gdal.GetDriverByName('ENVI').Create(out_path, cols, rows, 1, gdal.GDT_Byte)
+        od.SetGeoTransform(geo)
+        od.SetProjection(proj)
+        return od
+
+    try:
+        # ────────── 第一趟：抽样扫全局标量 ──────────
+        samples = []
+        for y in range(0, rows, block_size):
+            ys = min(block_size, rows - y)
+            for x in range(0, cols, block_size):
+                xs = min(block_size, cols - x)
+                blk = _read_fused(x, y, xs, ys)
+                mb = _read_mask(x, y, xs, ys)
+                sub = blk[::sample_every, ::sample_every]
+                mbs = mb[::sample_every, ::sample_every]
+                valid = mbs & (sub > 0) & np.isfinite(sub)
+                if valid.any():
+                    samples.append(sub[valid])
+        allv = np.concatenate(samples) if samples else None
+
+        mean_val = std_val = None
+        thr = None
+        otsu_thr = None
+        p2 = p98 = None
+
+        if allv is not None:
+            if method == 'zscore':
+                mean_val = float(np.mean(allv))
+                std_val = float(np.std(allv))
+                if std_val == 0:
+                    raise ValueError("耀斑波段标准差为 0，无法使用 Z-score 方法")
+                print(f"[两趟] Z-score 全局: 均值={mean_val:.4f}, 标准差={std_val:.4f}, "
+                      f"样本={allv.size}")
+            elif method == 'percentile':
+                thr = float(np.percentile(allv, percentile_value))
+                print(f"[两趟] 百分位数法: {percentile_value}% 分位={thr:.4f}, 样本={allv.size}")
+            elif method == 'iqr':
+                q1, q3 = np.percentile(allv, [25.0, 75.0])
+                thr = float(q3 + iqr_multiplier * (q3 - q1))
+                print(f"[两趟] IQR 法: Q1={q1:.4f}, Q3={q3:.4f}, 上界={thr:.4f}")
+            elif method == 'otsu':
+                p2, p98 = np.percentile(allv, [2.0, 98.0])
+                if p98 - p2 > 1e-12:
+                    scaled = (np.clip(allv, p2, p98) - p2) / (p98 - p2) * 255.0
+                    hist, _ = np.histogram(scaled, bins=256, range=(0.0, 255.0))
+                    otsu_thr = _otsu_threshold_from_hist(hist)
+                else:
+                    otsu_thr = 0.0
+                print(f"[两趟] Otsu 法: P2={p2:.4f}, P98={p98:.4f}, 阈值(0-255)={otsu_thr:.1f}")
+
+        # ────────── 创建输出 ──────────
+        od = _create_output()
+        ob = od.GetRasterBand(1)
+        ob.Fill(0)
+
+        # 无有效样本 → 全 0（无耀斑）
+        if allv is None:
+            ob.FlushCache()
+            od = None
+            print("警告: 水域内无有效正反射率样本，输出全 0 耀斑掩膜")
+            return out_path
+
+        # ────────── 第二趟：分块检测并写盘 ──────────
+        for y in range(0, rows, block_size):
+            ys = min(block_size, rows - y)
+            for x in range(0, cols, block_size):
+                xs = min(block_size, cols - x)
+                blk = _read_fused(x, y, xs, ys)
+                mb = _read_mask(x, y, xs, ys)
+
+                if method == 'zscore':
+                    zs = np.zeros_like(blk, dtype=np.float32)
+                    valid = mb & np.isfinite(blk)
+                    zs[valid] = (blk[valid] - mean_val) / std_val
+                    det = (zs > z_threshold)
+                elif method == 'percentile':
+                    det = mb & (blk > thr)
+                elif method == 'iqr':
+                    det = mb & (blk > thr)
+                elif method == 'otsu':
+                    if p98 is not None and p98 - p2 > 1e-12 and otsu_thr is not None:
+                        scaled = (np.clip(blk, p2, p98) - p2) / (p98 - p2) * 255.0
+                        det = mb & (scaled > otsu_thr)
+                    else:
+                        det = np.zeros(blk.shape, dtype=bool)
+                ob.WriteArray(det.astype(np.uint8), x, y)
+
+        ob.FlushCache()
+        od = None
+        print(f"[两趟] 耀斑掩膜已写出: {out_path} ({cols}x{rows}, uint8)")
+        return out_path
+    finally:
+        if wm_cleanup is not None:
+            _remove_raster_output(wm_cleanup)
+        ds = None
+
+
 @timeit
-def find_severe_glint_area(img_path, water_mask, glint_wave=750, output_path=None, 
+def find_severe_glint_area(img_path, water_mask, glint_wave=750, output_path=None,
                           method='otsu', multi_band_waves=None, **kwargs):
     """
     找到严重耀斑区域的主函数
@@ -604,6 +830,49 @@ def find_severe_glint_area(img_path, water_mask, glint_wave=750, output_path=Non
     num_bands = dataset.RasterCount
     im_width = dataset.RasterXSize
     im_height = dataset.RasterYSize
+
+    # ── ★ v3 两趟快速路径（内存安全）────────────────────────────
+    # 默认方法（otsu/zscore/percentile/iqr/multi_band）且未启用
+    # max_area/buffer_size 结构后处理时，直接走两趟分块检测，
+    # 全程不整幅驻留波段与掩膜。adaptive / 结构后处理仍走旧全幅路径。
+    max_area_cfg = kwargs.get('max_area', None)
+    buffer_cfg = kwargs.get('buffer_size', None)
+    post_needed = bool(max_area_cfg and max_area_cfg > 0) or \
+        bool(buffer_cfg and buffer_cfg > 0)
+
+    if not post_needed:
+        if method == 'multi_band':
+            waves = multi_band_waves if multi_band_waves else \
+                [glint_wave, glint_wave + 50, glint_wave + 100]
+            bands1 = []
+            for wv in waves:
+                bn = find_band_number(wv, img_path)
+                if 0 <= bn < dataset.RasterCount:
+                    bands1.append(bn + 1)
+            sub = kwargs.get('sub_method', 'zscore')
+            if bands1 and sub in ('zscore', 'percentile', 'iqr', 'otsu'):
+                _detect_glint_two_pass(
+                    img_path, water_mask, output_path, bands1,
+                    kwargs.get('weights', None), sub,
+                    z_threshold=kwargs.get('z_threshold', 2.5),
+                    percentile_value=kwargs.get('percentile', 95.0),
+                    iqr_multiplier=kwargs.get('iqr_multiplier', 1.5))
+                dataset = None
+                return output_path
+        elif method in ('zscore', 'percentile', 'iqr', 'otsu'):
+            bn = find_band_number(glint_wave, img_path)
+            if 0 <= bn < dataset.RasterCount:
+                _detect_glint_two_pass(
+                    img_path, water_mask, output_path, [bn + 1], None, method,
+                    z_threshold=kwargs.get('z_threshold', 2.5),
+                    percentile_value=kwargs.get('percentile', 95.0),
+                    iqr_multiplier=kwargs.get('iqr_multiplier', 1.5))
+                dataset = None
+                return output_path
+    else:
+        print("信息: 启用了连通域/岸线后处理，主检测走旧全幅路径。")
+        if im_height * im_width > 50_000_000:
+            print("警告: 影像超过 5000 万像素，后处理需整幅数组，内存占用会很大。")
     
     # 读取水域掩膜，如果water_mask为None或空字符串，则创建全图掩膜
     if water_mask is None or water_mask == "":