refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-sar
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title: SAR (Synthetic Aperture Radar)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Synthetic Aperture Radar, SAR imagery]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [sar, remote-sensing, radar, geospatial, deep-learning]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: SNAP / PyTorch
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---
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# SAR (Synthetic Aperture Radar)
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 물리
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- **Active sensor**: own microwave (C-band 5.4GHz, X-band 9.6GHz, L-band 1.3GHz) 송신, backscatter 측정.
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- **Synthetic aperture**: 매 platform motion 으로 매 large virtual antenna 합성 → sub-meter resolution.
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- **Polarimetry**: HH/HV/VH/VV → material/structure 정보.
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- **Interferometry (InSAR)**: phase difference 로 매 mm-level surface deformation.
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### 매 Data products
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- **SLC** (Single Look Complex): phase 보존, InSAR 용.
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- **GRD** (Ground Range Detected): amplitude only, 일반 분석.
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- **Speckle noise**: multiplicative, log-normal — 매 deep despeckling 의 핵심 challenge.
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### 매 응용
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1. Maritime surveillance (ship detection, dark-vessel via AIS-cross).
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2. Disaster response (flood mapping, earthquake deformation).
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3. Agriculture (crop type, soil moisture).
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4. Defense (change detection, target classification).
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5. Subsidence monitoring (city, mining, dam).
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## 💻 패턴
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### Sentinel-1 download (sentinelsat)
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```python
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from sentinelsat import SentinelAPI
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from datetime import date
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api = SentinelAPI("user", "pass", "https://apihub.copernicus.eu/apihub")
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products = api.query(
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footprint="POLYGON((127 37, 128 37, 128 38, 127 38, 127 37))",
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date=(date(2026, 1, 1), date(2026, 5, 1)),
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platformname="Sentinel-1",
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producttype="GRD",
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)
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api.download_all(products)
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```
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### Speckle filtering (Refined Lee)
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```python
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import numpy as np
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from scipy.ndimage import generic_filter
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def refined_lee(img, size=7):
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def filter_fn(window):
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mean = window.mean()
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var = window.var()
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cu = 0.523 # SAR ENL-derived
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ci = np.sqrt(var) / mean if mean else 0
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w = max(0, (ci**2 - cu**2) / (ci**2 * (1 + cu**2)))
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return mean + w * (window[len(window)//2] - mean)
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return generic_filter(img, filter_fn, size=size)
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```
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### Deep despeckling (SAR-CNN, 2026 SOTA)
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```python
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import torch.nn as nn
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class SARCNN(nn.Module):
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def __init__(self, depth=17):
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super().__init__()
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layers = [nn.Conv2d(1, 64, 3, padding=1), nn.ReLU()]
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for _ in range(depth - 2):
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layers += [nn.Conv2d(64, 64, 3, padding=1),
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nn.BatchNorm2d(64), nn.ReLU()]
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layers.append(nn.Conv2d(64, 1, 3, padding=1))
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self.net = nn.Sequential(*layers)
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def forward(self, x):
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# log domain — speckle becomes additive
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return torch.log(x + 1e-6) - self.net(torch.log(x + 1e-6))
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```
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### Ship detection (CFAR + YOLO-SAR)
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```python
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from ultralytics import YOLO
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# Fine-tuned on SSDD/HRSID dataset
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model = YOLO("yolov8-sar-ship.pt")
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results = model("sentinel1_grd_tile.tif", conf=0.4, imgsz=1024)
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for box in results[0].boxes:
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lon, lat = pixel_to_geo(box.xywh[0][:2])
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print(f"Ship @ {lat:.4f},{lon:.4f} conf={box.conf.item():.2f}")
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```
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### InSAR coherence + interferogram (snappy)
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```python
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# ESA SNAP via snappy: master/slave coregistration → ifg
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from snappy import GPF, ProductIO
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master = ProductIO.readProduct("S1A_master.zip")
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slave = ProductIO.readProduct("S1A_slave.zip")
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coreg = GPF.createProduct("Back-Geocoding", params, [master, slave])
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ifg = GPF.createProduct("Interferogram", {"includeCoherence": True}, coreg)
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ProductIO.writeProduct(ifg, "ifg.dim", "BEAM-DIMAP")
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```
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### Flood change detection
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```python
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import rasterio
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import numpy as np
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with rasterio.open("pre.tif") as a, rasterio.open("post.tif") as b:
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pre, post = a.read(1), b.read(1)
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# Log-ratio
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lr = np.log10(post / (pre + 1e-6))
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flood_mask = lr < -0.5 # darker = water in VV
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Free, weekly revisit | Sentinel-1 GRD |
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| Sub-daily, sub-meter | Capella / ICEYE / Umbra commercial |
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| Deformation (mm) | InSAR time series (Sentinel-1, ALOS-2 L-band) |
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| Foliage penetration | L-band (ALOS, NISAR 2026) |
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| Maritime wide-area | Sentinel-1 EW + AIS fusion |
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**기본값**: Sentinel-1 GRD + deep despeckling + YOLO-SAR for object tasks.
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## 🔗 Graph
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## 🤖 LLM 활용
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**언제**: report generation from detection outputs, multi-modal SAR+optical fusion via VLM (Prithvi-SAR, 2026), tasking-orchestration agents.
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**언제 X**: pixel-level despeckling/segmentation — use specialized CNN/transformer, not LLM.
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## ❌ 안티패턴
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- **Speckle ignored**: training optical CNN directly on SAR amplitude — speckle dominates loss.
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- **No log/dB conversion**: SAR has 60+ dB dynamic range; visualize/train in dB scale.
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- **Geocoding skipped**: pixel coords ≠ geographic — terrain correction required.
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- **Single polarization**: dual-pol (VV+VH) gives material discrimination essentially free.
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## 🧪 검증 / 중복
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- Verified (ESA Copernicus, NASA NISAR docs, Capella tech papers).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — SAR physics, deep despeckling, ship/flood detection |
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