---
id: wiki-2026-0508-sar
title: SAR (Synthetic Aperture Radar)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Synthetic Aperture Radar, SAR imagery]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [sar, remote-sensing, radar, geospatial, deep-learning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: SNAP / PyTorch
---

# SAR (Synthetic Aperture Radar)

## 매 한 줄
> **"매 active microwave imaging — 매 cloud/night 무관 의 24/7 Earth observation"**. 매 1950s aircraft radar 로 시작 → 매 Sentinel-1 (ESA, free), Capella, ICEYE, Umbra 등 매 commercial smallsat constellation 으로 daily revisit. 매 deep learning (despeckling, segmentation, change detection) 이 매 traditional SAR processing pipeline 을 재편.

## 매 핵심

### 매 물리
- **Active sensor**: own microwave (C-band 5.4GHz, X-band 9.6GHz, L-band 1.3GHz) 송신, backscatter 측정.
- **Synthetic aperture**: 매 platform motion 으로 매 large virtual antenna 합성 → sub-meter resolution.
- **Polarimetry**: HH/HV/VH/VV → material/structure 정보.
- **Interferometry (InSAR)**: phase difference 로 매 mm-level surface deformation.

### 매 Data products
- **SLC** (Single Look Complex): phase 보존, InSAR 용.
- **GRD** (Ground Range Detected): amplitude only, 일반 분석.
- **Speckle noise**: multiplicative, log-normal — 매 deep despeckling 의 핵심 challenge.

### 매 응용
1. Maritime surveillance (ship detection, dark-vessel via AIS-cross).
2. Disaster response (flood mapping, earthquake deformation).
3. Agriculture (crop type, soil moisture).
4. Defense (change detection, target classification).
5. Subsidence monitoring (city, mining, dam).

## 💻 패턴

### Sentinel-1 download (sentinelsat)
```python
from sentinelsat import SentinelAPI
from datetime import date

api = SentinelAPI("user", "pass", "https://apihub.copernicus.eu/apihub")
products = api.query(
    footprint="POLYGON((127 37, 128 37, 128 38, 127 38, 127 37))",
    date=(date(2026, 1, 1), date(2026, 5, 1)),
    platformname="Sentinel-1",
    producttype="GRD",
)
api.download_all(products)
```

### Speckle filtering (Refined Lee)
```python
import numpy as np
from scipy.ndimage import generic_filter

def refined_lee(img, size=7):
    def filter_fn(window):
        mean = window.mean()
        var = window.var()
        cu = 0.523  # SAR ENL-derived
        ci = np.sqrt(var) / mean if mean else 0
        w = max(0, (ci**2 - cu**2) / (ci**2 * (1 + cu**2)))
        return mean + w * (window[len(window)//2] - mean)
    return generic_filter(img, filter_fn, size=size)
```

### Deep despeckling (SAR-CNN, 2026 SOTA)
```python
import torch.nn as nn

class SARCNN(nn.Module):
    def __init__(self, depth=17):
        super().__init__()
        layers = [nn.Conv2d(1, 64, 3, padding=1), nn.ReLU()]
        for _ in range(depth - 2):
            layers += [nn.Conv2d(64, 64, 3, padding=1),
                       nn.BatchNorm2d(64), nn.ReLU()]
        layers.append(nn.Conv2d(64, 1, 3, padding=1))
        self.net = nn.Sequential(*layers)

    def forward(self, x):
        # log domain — speckle becomes additive
        return torch.log(x + 1e-6) - self.net(torch.log(x + 1e-6))
```

### Ship detection (CFAR + YOLO-SAR)
```python
from ultralytics import YOLO

# Fine-tuned on SSDD/HRSID dataset
model = YOLO("yolov8-sar-ship.pt")
results = model("sentinel1_grd_tile.tif", conf=0.4, imgsz=1024)
for box in results[0].boxes:
    lon, lat = pixel_to_geo(box.xywh[0][:2])
    print(f"Ship @ {lat:.4f},{lon:.4f} conf={box.conf.item():.2f}")
```

### InSAR coherence + interferogram (snappy)
```python
# ESA SNAP via snappy: master/slave coregistration → ifg
from snappy import GPF, ProductIO

master = ProductIO.readProduct("S1A_master.zip")
slave = ProductIO.readProduct("S1A_slave.zip")
coreg = GPF.createProduct("Back-Geocoding", params, [master, slave])
ifg = GPF.createProduct("Interferogram", {"includeCoherence": True}, coreg)
ProductIO.writeProduct(ifg, "ifg.dim", "BEAM-DIMAP")
```

### Flood change detection
```python
import rasterio
import numpy as np

with rasterio.open("pre.tif") as a, rasterio.open("post.tif") as b:
    pre, post = a.read(1), b.read(1)
# Log-ratio
lr = np.log10(post / (pre + 1e-6))
flood_mask = lr < -0.5  # darker = water in VV
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Free, weekly revisit | Sentinel-1 GRD |
| Sub-daily, sub-meter | Capella / ICEYE / Umbra commercial |
| Deformation (mm) | InSAR time series (Sentinel-1, ALOS-2 L-band) |
| Foliage penetration | L-band (ALOS, NISAR 2026) |
| Maritime wide-area | Sentinel-1 EW + AIS fusion |

**기본값**: Sentinel-1 GRD + deep despeckling + YOLO-SAR for object tasks.

## 🔗 Graph
- 부모: [[Remote-Sensing]] · [[Geospatial-AI]]
- 변형: [[InSAR]] · [[Polarimetric-SAR]] · [[Bistatic-SAR]]
- 응용: [[Maritime-Domain-Awareness]] · [[Flood-Mapping]] · [[Subsidence-Monitoring]]
- Adjacent: [[Optical-Remote-Sensing]] · [[Foundation-Models-Geo]] · [[Earth-Observation]]

## 🤖 LLM 활용
**언제**: report generation from detection outputs, multi-modal SAR+optical fusion via VLM (Prithvi-SAR, 2026), tasking-orchestration agents.
**언제 X**: pixel-level despeckling/segmentation — use specialized CNN/transformer, not LLM.

## ❌ 안티패턴
- **Speckle ignored**: training optical CNN directly on SAR amplitude — speckle dominates loss.
- **No log/dB conversion**: SAR has 60+ dB dynamic range; visualize/train in dB scale.
- **Geocoding skipped**: pixel coords ≠ geographic — terrain correction required.
- **Single polarization**: dual-pol (VV+VH) gives material discrimination essentially free.

## 🧪 검증 / 중복
- Verified (ESA Copernicus, NASA NISAR docs, Capella tech papers).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — SAR physics, deep despeckling, ship/flood detection |
