How unit 2 is examined
This unit covers sensors, platforms, resolution, pre-processing, corrections and fusion; no topic was asked in the supplied papers, so learn the definitions, classifications and lists below.
Sensors: types and classification
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>A sensor is a device that detects and records electromagnetic radiation reflected or emitted by an object and converts it into a signal that can be stored as an image or data.</mark>
Diagram. <figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u2-01" viewBox="0 0 816 258" width="816" height="258" role="img" aria-label="Classification of sensors by energy source"><style>#dsfig-u2-01 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u2-01 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u2-01 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u2-01 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u2-01 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u2-01 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u2-01 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u2-01 .t{fill:#16181D;font-weight:500}#dsfig-u2-01 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u2-01 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u2-01 .dot{fill:#16181D}#dsfig-u2-01 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u2-01 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u2-01 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u2-01 .ah{fill:#454C5A}#dsfig-u2-01 .ah.hi{fill:#2340B8}#dsfig-u2-01 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u2-01 .wl .t{font-size:12px;font-weight:700}#dsfig-u2-01 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u2-01 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u2-01 .e{stroke:#B1B7C3}html.dark #dsfig-u2-01 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u2-01 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u2-01 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u2-01 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u2-01 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u2-01 .t{fill:#E6E8ED}html.dark #dsfig-u2-01 .t.inv{fill:#0F1115}html.dark #dsfig-u2-01 .kd{stroke:#E6E8ED}html.dark #dsfig-u2-01 .dot{fill:#E6E8ED}html.dark #dsfig-u2-01 .ann{fill:#8FA3FF}html.dark #dsfig-u2-01 .lbl{fill:#858D9C}html.dark #dsfig-u2-01 .ptr{fill:#8FA3FF}html.dark #dsfig-u2-01 .ah{fill:#B1B7C3}html.dark #dsfig-u2-01 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u2-01 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u2-01 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u2-01 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah4" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah" d="M0,1 L9,5 L0,9 z"/></marker><marker id="ahh4" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah hi" d="M0,1 L9,5 L0,9 z"/></marker></defs><line class="e" x1="462.6" y1="37" x2="462.6" y2="101"/><line class="e" x1="462.6" y1="101" x2="259.8" y2="165"/><line class="e" x1="462.6" y1="101" x2="665.5" y2="165"/><line class="e" x1="259.8" y1="165" x2="79" y2="229"/><line class="e" x1="259.8" y1="165" x2="252" y2="229"/><line class="e" x1="259.8" y1="165" x2="440.5" y2="229"/><line class="e" x1="665.5" y1="165" x2="582.5" y2="229"/><line class="e" x1="665.5" y1="165" x2="673.5" y2="229"/><line class="e" x1="665.5" y1="165" x2="748.5" y2="229"/><rect class="n" x="425.1" y="22" width="75" height="30" rx="8"/><text class="t" x="462.6" y="37" dy=".35em" text-anchor="middle">Sensors</text><rect class="n" x="397.6" y="86" width="130" height="30" rx="8"/><text class="t" x="462.6" y="101" dy=".35em" text-anchor="middle">Remote sensors</text><rect class="n" x="222.3" y="150" width="75" height="30" rx="8"/><text class="t" x="259.8" y="165" dy=".35em" text-anchor="middle">Passive</text><rect class="n" x="14" y="214" width="130" height="30" rx="8"/><text class="t" x="79" y="229" dy=".35em" text-anchor="middle">Optical camera</text><rect class="n" x="160" y="214" width="184" height="30" rx="8"/><text class="t" x="252" y="229" dy=".35em" text-anchor="middle">Multispectral scanner</text><rect class="n" x="360" y="214" width="161" height="30" rx="8"/><text class="t" x="440.5" y="229" dy=".35em" text-anchor="middle">Thermal radiometer</text><rect class="n" x="632" y="150" width="67" height="30" rx="8"/><text class="t" x="665.5" y="165" dy=".35em" text-anchor="middle">Active</text><rect class="n" x="537" y="214" width="91" height="30" rx="8"/><text class="t" x="582.5" y="229" dy=".35em" text-anchor="middle">Radar/SAR</text><rect class="n" x="644" y="214" width="59" height="30" rx="8"/><text class="t" x="673.5" y="229" dy=".35em" text-anchor="middle">LiDAR</text><rect class="n" x="719" y="214" width="59" height="30" rx="8"/><text class="t" x="748.5" y="229" dy=".35em" text-anchor="middle">Sonar</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">Classification of sensors by energy source</figcaption></figure>
Key points.
- Passive sensors record natural energy, mostly reflected sunlight or emitted heat, so optical cameras and thermal scanners work only with a natural source.
- Active sensors supply their own energy and measure the return, as in radar (SAR) and LiDAR, so they work day and night and radar works through cloud.
- By imaging method, sensors are framing (a camera records the whole scene at once) or scanning (whiskbroom uses a rotating mirror, pushbroom uses a line array of detectors).
- By spectral range they are optical, thermal infrared or microwave; by number of bands they are panchromatic (one broad band), multispectral (3 to 10 bands) or hyperspectral (hundreds of narrow bands).
| Basis | Types |
|---|---|
| Energy source | Passive, active |
| Recording | Imaging, non-imaging (for example a profiling radiometer) |
| Scanning | Framing, whiskbroom, pushbroom |
| Bands | Panchromatic, multispectral, hyperspectral |
Platforms: ground, airborne and spaceborne
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>A platform is the structure that carries the sensor at a suitable height above the target.</mark>
Diagram. <figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u2-02" viewBox="0 0 906 194" width="906" height="194" role="img" aria-label="Types of remote sensing platforms"><style>#dsfig-u2-02 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u2-02 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u2-02 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u2-02 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u2-02 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u2-02 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u2-02 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u2-02 .t{fill:#16181D;font-weight:500}#dsfig-u2-02 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u2-02 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u2-02 .dot{fill:#16181D}#dsfig-u2-02 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u2-02 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u2-02 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u2-02 .ah{fill:#454C5A}#dsfig-u2-02 .ah.hi{fill:#2340B8}#dsfig-u2-02 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u2-02 .wl .t{font-size:12px;font-weight:700}#dsfig-u2-02 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u2-02 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u2-02 .e{stroke:#B1B7C3}html.dark #dsfig-u2-02 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u2-02 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u2-02 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u2-02 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u2-02 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u2-02 .t{fill:#E6E8ED}html.dark #dsfig-u2-02 .t.inv{fill:#0F1115}html.dark #dsfig-u2-02 .kd{stroke:#E6E8ED}html.dark #dsfig-u2-02 .dot{fill:#E6E8ED}html.dark #dsfig-u2-02 .ann{fill:#8FA3FF}html.dark #dsfig-u2-02 .lbl{fill:#858D9C}html.dark #dsfig-u2-02 .ptr{fill:#8FA3FF}html.dark #dsfig-u2-02 .ah{fill:#B1B7C3}html.dark #dsfig-u2-02 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u2-02 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u2-02 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u2-02 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah5" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah" d="M0,1 L9,5 L0,9 z"/></marker><marker id="ahh5" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah hi" d="M0,1 L9,5 L0,9 z"/></marker></defs><line class="e" x1="467.6" y1="37" x2="174" y2="101"/><line class="e" x1="467.6" y1="37" x2="480.5" y2="101"/><line class="e" x1="467.6" y1="37" x2="761.3" y2="101"/><line class="e" x1="174" y1="101" x2="59.5" y2="165"/><line class="e" x1="174" y1="101" x2="178" y2="165"/><line class="e" x1="174" y1="101" x2="288.5" y2="165"/><line class="e" x1="480.5" y1="101" x2="383.5" y2="165"/><line class="e" x1="480.5" y1="101" x2="478.5" y2="165"/><line class="e" x1="480.5" y1="101" x2="577.5" y2="165"/><line class="e" x1="761.3" y1="101" x2="700" y2="165"/><line class="e" x1="761.3" y1="101" x2="822.5" y2="165"/><rect class="n" x="422.1" y="22" width="91" height="30" rx="8"/><text class="t" x="467.6" y="37" dy=".35em" text-anchor="middle">Platforms</text><rect class="n" x="140.5" y="86" width="67" height="30" rx="8"/><text class="t" x="174" y="101" dy=".35em" text-anchor="middle">Ground</text><rect class="n" x="14" y="150" width="91" height="30" rx="8"/><text class="t" x="59.5" y="165" dy=".35em" text-anchor="middle">Hand-held</text><rect class="n" x="121" y="150" width="114" height="30" rx="8"/><text class="t" x="178" y="165" dy=".35em" text-anchor="middle">Tripod/tower</text><rect class="n" x="251" y="150" width="75" height="30" rx="8"/><text class="t" x="288.5" y="165" dy=".35em" text-anchor="middle">Vehicle</text><rect class="n" x="439" y="86" width="83" height="30" rx="8"/><text class="t" x="480.5" y="101" dy=".35em" text-anchor="middle">Airborne</text><rect class="n" x="342" y="150" width="83" height="30" rx="8"/><text class="t" x="383.5" y="165" dy=".35em" text-anchor="middle">Aircraft</text><rect class="n" x="441" y="150" width="75" height="30" rx="8"/><text class="t" x="478.5" y="165" dy=".35em" text-anchor="middle">Balloon</text><rect class="n" x="532" y="150" width="91" height="30" rx="8"/><text class="t" x="577.5" y="165" dy=".35em" text-anchor="middle">Drone/UAV</text><rect class="n" x="712.3" y="86" width="98" height="30" rx="8"/><text class="t" x="761.3" y="101" dy=".35em" text-anchor="middle">Spaceborne</text><rect class="n" x="639" y="150" width="122" height="30" rx="8"/><text class="t" x="700" y="165" dy=".35em" text-anchor="middle">Space shuttle</text><rect class="n" x="777" y="150" width="91" height="30" rx="8"/><text class="t" x="822.5" y="165" dy=".35em" text-anchor="middle">Satellite</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">Types of remote sensing platforms</figcaption></figure>
Key points.
- Ground-based platforms (hand-held, tripod, tower, vehicle) sit close to the target and give detailed field data, and are used to calibrate satellite data.
- Airborne platforms (aircraft, balloons, drones) fly from a few hundred metres to about 20 km and give high-resolution, on-demand coverage of small areas.
- Spaceborne platforms (satellites) give repeated, wide and systematic coverage; a geostationary satellite sits at about 36,000 km, while a polar sun-synchronous satellite orbits at about 700 to 900 km.
- Higher platforms cover more area but with coarser detail, while lower platforms give finer detail over a smaller area.
| Platform | Height | Coverage | Detail |
|---|---|---|---|
| Ground | metres | tiny | very fine |
| Airborne | 0.1 to 20 km | local | fine |
| Spaceborne | 700 to 36,000 km | regional to global | coarse to medium |
Data acquisition techniques and image resolution
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>Data acquisition is the process of sensing and recording energy from the scene, and image resolution is the level of detail the sensor can capture.</mark>
Key points.
- Acquisition follows the chain source, atmosphere, target, sensor, ground station; the recorded signal is digitised into pixels called digital numbers (DN).
- Spatial resolution is the ground size of one pixel (30 m for Landsat multispectral), and smaller pixels show finer objects.
- Spectral resolution is the number and narrowness of the wavelength bands recorded, and more bands separate materials better.
- Radiometric resolution is the number of brightness levels, set by bits: $2^{n}$ levels for $n$ bits, so 8 bits give $2^8=256$ levels.
- Temporal resolution is the revisit interval over the same area (16 days for Landsat).
Example. A 6-bit sensor records $2^6=64$ grey levels; an 11-bit sensor records $2^{11}=2048$.
Data pre-processing and feature extraction techniques
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>Pre-processing prepares raw imagery by removing errors and noise, and feature extraction derives useful measurements from it, such as indices, texture and edges.</mark>
Key points.
- Pre-processing includes radiometric and geometric correction, noise removal, mosaicking (joining scenes) and clipping to the study area.
- Spectral features include band ratios and indices such as $NDVI=\frac{NIR-Red}{NIR+Red}$, which highlights healthy vegetation.
- Spatial features include texture, edges and shape, obtained by filters and by segmentation of the image into objects.
- Principal component analysis (PCA) reduces many correlated bands to a few uncorrelated components, which cuts data volume.
- Extracted features are the input to the AI classifiers used in later units.
Example. If $NIR=0.50$ and $Red=0.10$, then $NDVI=\frac{0.50-0.10}{0.50+0.10}=0.67$, which indicates dense vegetation.
Radiometric and geometric corrections
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>Radiometric correction removes errors in pixel brightness, and geometric correction removes spatial distortion so that the image fits map coordinates.</mark>
Key points.
- Radiometric correction covers sensor calibration (DN to radiance), atmospheric correction for haze and scattering, sun-angle correction, and repair of striping or dropped lines.
- A simple atmospheric method is dark-object subtraction: subtract the darkest pixel value of a band from every pixel of that band.
- Geometric errors come from Earth rotation, panoramic view, platform motion (altitude and attitude changes) and terrain relief.
- Geometric correction picks ground control points (GCPs), fits a transformation (polynomial) between image and map, and then resamples the pixels.
- Resampling is by nearest neighbour (keeps original values), bilinear (4 neighbours) or cubic convolution (16 neighbours, smoothest).
| Radiometric | Geometric | |
|---|---|---|
| Fixes | Brightness values | Position and shape |
| Causes | Atmosphere, sensor, sun angle | Earth rotation, platform motion, relief |
| Method | Calibration, dark-object subtraction | GCPs, transformation, resampling |
Data fusion and enhancement methods
<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>
Definition. <mark>Data fusion combines images from different sensors or resolutions into one richer image, and enhancement improves the visual contrast and interpretability of an image.</mark>
Key points.
- Pan-sharpening fuses a high-resolution panchromatic band with low-resolution multispectral bands to give a sharp coloured image, using methods such as IHS, Brovey or PCA.
- Fusion can be done at pixel level, feature level or decision level, and can join optical with radar or LiDAR data.
- Contrast enhancement uses linear stretch and histogram equalization to spread the brightness values over the full display range.
- Spatial enhancement uses filters: a low-pass filter smooths noise and a high-pass filter sharpens edges.
Last-minute revision
- A sensor records EM radiation; a platform carries the sensor.
- Passive sensors use the Sun; active sensors (radar, LiDAR) carry their own source.
- Platforms by height: ground, airborne (drones, aircraft), spaceborne (satellites).
- Geostationary orbit is about 36,000 km; polar sun-synchronous orbit is about 700 to 900 km.
- Four resolutions: spatial, spectral, radiometric, temporal.
- An $n$-bit sensor has $2^n$ grey levels, so 8 bits give 256.
- Landsat has 30 m pixels and a 16-day revisit.
- $NDVI=(NIR-Red)/(NIR+Red)$.
- Radiometric correction fixes brightness; geometric correction fixes position.
- Resampling methods: nearest neighbour, bilinear, cubic convolution.
- Pan-sharpening fuses a sharp panchromatic band with coloured multispectral bands.
Memory hooks
- SRTS: Spatial, Radiometric, Temporal, Spectral resolutions.
- Passive waits for the Sun; active shouts and listens.
- Ground, Air, Space: the higher, the wider but the coarser.
- Radiometric = Radiance (brightness); Geometric = Geography (position).
- Resampling 1-4-16: nearest 1 pixel, bilinear 4, cubic 16.
Coverage checklist
- Sensors: Types and classification of sensors (no past questions).
- Platforms: Types of platforms, ground, airborne, and space born platforms (no past questions).
- Data acquisition techniques and image resolution (no past questions).
- Data pre-processing and feature extraction techniques (no past questions).
- Radiometric and geometric corrections (no past questions).
- Data fusion and enhancement methods (no past questions).