Thermal Academy · Technical Fundamentals
Put a 384×288 thermal beside a 640×512 and the spec sheet makes the answer look obvious. Six-forty has more pixels, so it must see further. Buy the bigger number, done.
Not quite. A higher-resolution detector hands the imager more spatial information to work with, and that can preserve target detail at distance, sharpen identification and give digital zoom something real to enlarge. But resolution never works alone. Put a 640 behind the wrong lens and it can be less useful for a particular job than a well-matched 384. The question that actually decides what you see in a paddock at 3am isn’t “how many pixels does the sensor have?” It’s how many of those pixels are landing on the animal.
Resolution isn’t range. It’s how many pixels you can put on the target.
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The short version
- Resolution is the number of individual infrared detectors in the sensor — 640×512 means 640 across, 512 down, about 328,000 tiny thermometers taking a reading every frame.
- More pixels generally mean more spatial detail. A 640 holds nearly three times the detector pixels of a 384, and a 1280 holds about four times the pixels of a 640.
- More pixels do not automatically mean proportionally more range. Three times the pixels is not three times the reach.
- The lens decides how tightly those pixels are projected onto the scene; pixel pitch sets how big each detector is; NETD decides how faint a temperature difference each pixel can reveal.
- Higher resolution helps most with recognition, identification and digital zoom — the hard part — more than with raw “something’s out there” detection.
- The best thermal is a balanced optical system, not the biggest resolution number on the box.
What does thermal resolution actually mean?
Thermal resolution is the number of individual infrared detectors packed into the sensor. Nothing more mysterious than that. A microbolometer is a grid of tiny heat-sensing elements, and each one takes its own temperature reading, hundreds of thousands of times a second. When a spec sheet says 640×512, it means the detector is 640 elements wide and 512 tall. Multiply them and you get the total: about 328,000 separate measurements of the scene, every single frame.
That word — measurements — is the whole idea. A thermal image is not a photograph. It is a mosaic built from a fixed number of temperature samples, and resolution tells you how many samples the sensor is allowed to take. Here is the full ladder the industry actually sells, from entry level to specialist:
| Resolution class | Grid | Total detector pixels | The maths |
|---|---|---|---|
| 256 class | 256×192 | 49,152 | 256 × 192 |
| 384 class | 384×288 | 110,592 | 384 × 288 |
| 640 class | 640×512 | 327,680 | 640 × 512 |
| 1024 class | 1024×768 | 786,432 | 1024 × 768 |
| 1280 class (SXGA) | 1280×1024 | 1,310,720 | 1280 × 1024 |
Every step up the ladder is a jump in the number of infrared measurements made per frame — from about 49 thousand at the bottom to over 1.3 million at the top.
A thermal sensor is simply an array of these individual infrared detectors. If you want the physics of how one pixel turns landed infrared into a number — the suspended bridge, the vanadium-oxide film, the vacuum package — that is its own lesson: how a thermal sensor actually sees heat. For this guide, hold one picture in your head: a grid of tiny thermometers, and resolution is how many are in the grid.
Resolution is not the size of the image on your screen
This is the single most common mix-up, and manufacturers rarely rush to clear it up. There are actually four different “resolutions” in a thermal device, and only one of them is the sensor.
| Which resolution | What it is | Who it belongs to |
|---|---|---|
| Detector resolution | The real infrared sampling elements — the only true “thermal” resolution | The microbolometer |
| Display resolution | The OLED or LCOS screen your eye looks at through the eyepiece | The viewfinder |
| Output / video resolution | The dimensions of recorded or streamed footage | The processor and encoder |
| Upscaled resolution | Software-created pixels, invented to fill a bigger screen | The image-processing pipeline |
A brochure that leads with a big “1920×1080” is usually quoting the display or the video, not the detector. Always find the detector line.
We will come back to that word — interpolation — when we talk about digital zoom, because it is exactly the same trick, and understanding it once explains both.
Why do more detector pixels actually matter?
If more pixels don’t simply mean more range, what do they buy you? Four things, and they build on each other.
More spatial detail. Each pixel is one sample of the scene. Double the pixels across a target and you have twice as many samples describing its shape, so edges stay crisp for longer and fine structure survives out to greater distance.
Better recognition. Detecting a warm blob takes very few pixels. Working out that the blob is four-legged, dog-sized and moving like a fox rather than a cat takes many more. Extra resolution is what turns “something’s there” into “that’s a fox.”
Better identification. Target identification is the most spatially demanding task of all. Distinguishing the features needed to tell one target from another requires substantially more sampled detail than simply detecting or broadly recognising it. This is where additional usable resolution can become particularly valuable.
More usable digital zoom. Digital zoom crops into the pixels you already have. Start with more of them and you can enlarge further before the picture turns to blocks. A 640 simply has more to give away than a 384.
Underneath all four is one idea: information preservation. Resolution decides how much of the scene’s spatial information the sensor captures in the first place. You can enhance, sharpen and enlarge that information later, but you can only ever work with what was sampled.
So does higher thermal resolution let you see further?
It depends entirely on what you mean by “see.” This is the most-searched question about resolution and also the most mangled, because “seeing” a target is really three separate jobs, and resolution helps each one very differently.
| Task | What you can say | How pixel-hungry |
|---|---|---|
| Detection | “There’s something warm out there.” | Low — a couple of pixels can do it |
| Recognition | “That’s an animal — looks like a pig.” | Moderate — needs several times more |
| Identification | “There is enough spatial information to distinguish identifying features.” | High — the most pixels of all |
Detection is easy. Identification is hard. Resolution helps the hard end far more than the easy end.
Because detection needs so few pixels, a big warm animal can trip a “detect” at genuinely long range on almost any modern sensor. That is why headline “detection range” numbers look so impressive and mean so little. The range you would actually act at — identification — is always a fraction of that. Extra resolution can extend it, but only when the designer spends those pixels on sampling the target rather than on a wider view; with the same lens and pitch, more resolution mostly widens the field of view instead. Used that way, higher resolution pushes the “I know exactly what that is” distance out — the one that matters — more than it pushes the “something’s there” distance.
The idea that matters most: pixels on target
Resolution is step one of a pipeline. It sets the raw number of measurements available, but several stages after it decide how many of those measurements actually land on the thing you care about.
Here is the sentence to keep: a sensor’s headline resolution matters far less in isolation than how many of its pixels represent the target at the distance you’re shooting. That count is what decides detect, recognise or identify. And it is set by four things together — the target’s size, the lens focal length, the range, and the pixel pitch:
Watch how the same 640 sensor behaves as only the distance changes. Take a fox (roughly 0.4 m across), a 50 mm lens and a 12 µm pitch:
At 100 m the fox spans about 17 pixels — comfortably into identification territory. Walk it out to 200 m and the same fox on the same sensor now spans about 8 pixels — you can recognise it, but positive ID is slipping away. The sensor never changed. The pixels-on-target halved because the target got angularly smaller.
Now hold the distance at 150 m and change only the lens. A 25 mm lens puts about 6 pixels on that fox; a 50 mm lens puts about 11. Same 640 sensor, same night, same animal — nearly double the usable detail, purely from optics.
What is IFOV, and why should a hunter care?
IFOV — instantaneous field of view — is the plain-English bridge between resolution and detail. It is the angular slice of the world that a single detector pixel is responsible for.
Run the numbers on a 12 µm pixel behind a 50 mm lens: 0.012 mm ÷ 50 mm gives about 0.24 milliradians per pixel. Swap to a 25 mm lens and IFOV doubles to about 0.48 mrad — each pixel now covers twice the angular patch, so the same fox is described by half as many pixels. That is the same result we just saw in the bar chart, arriving from the other direction.
Resolution vs lens size
A 640 behind a 25 mm lens and a 640 behind a 50 mm lens are not the same tool. The sensor is identical; the lens changes how widely those pixels are spread across the world. The short lens spreads them over a wide scene — great for finding movement, thin on distant detail. The long lens concentrates them onto a narrower slice — fewer square metres in view, but many more pixels on anything far away.
So a wider field of view is not automatically “less capable” — it is a different job. This is exactly why the resolution number can’t be read alone, and it is the whole subject of two sibling lessons: how lens size changes what you see and why a wider view can beat more magnification.
Resolution vs pixel pitch
These two get confused constantly, and they are not the same thing. Resolution tells you how many pixels. Pixel pitch tells you how big each one is. A 12 µm pixel is physically smaller than a 17 µm pixel, so a 640×512 built on 12 µm pixels is a physically smaller chip than a 640×512 built on 17 µm — same pixel count, different real estate.
That physical size feeds straight back into IFOV and lens design. A smaller pitch narrows each pixel’s slice of the scene for a given lens, which can mean more detail — but it also collects less infrared energy per pixel, which puts pressure on sensitivity. So “12 µm is always better than 17 µm” is a trap; it is a trade-off, not an upgrade. The dedicated lesson walks the whole balance: 12µm vs 17µm pixel pitch.
Resolution vs NETD (this is the big one)
Resolution and NETD are the two halves of image quality, and they measure completely different things. Resolution is spatial information — how finely the scene is divided. NETD is thermal information — how subtle a temperature difference each of those divisions can actually reveal.
Get one without the other and the image suffers. A high-resolution sensor with poor sensitivity can hand you a crisp grid of samples that all read nearly the same — sharp, but flat and washed out when thermal contrast is low. A lower-resolution sensor with excellent sensitivity can separate tiny temperature differences beautifully, giving clean tonal separation — but it cannot invent spatial detail it never sampled. Neither number wins alone.
Resolution vs image processing
Between the detector and your eye sits a lot of software: sharpening, edge enhancement, noise reduction, temporal averaging, and increasingly AI-assisted “super-resolution.” Good processing is genuinely valuable — it makes the information the sensor captured easier for your eye to read, cleans up noise, and lifts faint edges.
But there is a hard ceiling. Processing can present existing information better; it cannot reliably recover unlimited physical detail the detector never sampled. Upscaling and super-resolution make an educated guess at what belongs between real samples. That can look cleaner and is often worth having — just don’t mistake a well-guessed pixel for a measured one. Two identical 640 sensors can produce visibly different images purely because of the processing behind them, which is a whole lesson of its own: why the software creates the image.
What actually happens when you digitally zoom?
Digital zoom feels like reaching further. It isn’t. Digital zoom crops into the pixels you already captured and enlarges them to fill the screen. No new detector pixels are created — you are just spreading fewer real samples across more glass.
This is the honest case for resolution and zoom: a higher-resolution detector gives you more native pixels to give away before the displayed target goes soft. It still isn’t magnification. The full treatment — and why 8× digital rarely helps — is here: optical vs digital zoom.
Play with it: the thermal resolution calculator
Everything above becomes obvious the moment you can move the sliders yourself. Pick a sensor, a pixel pitch, a lens, a target and a range, and watch two things move independently: the field of view (set by the sensor and lens) and the pixels landing on the target (set by lens, pitch, range and target). Change the sensor alone and notice how little the pixels-on-target figure moves — that is the entire lesson, live.
Thermal resolution & pixels-on-target explorer
Illustrative only — a simplified optical model to show how the factors interact, not a performance guarantee for any specific product.
Change the sensor and the field of view moves, but pixels-on-target barely does. Change the lens, pitch or range and pixels-on-target jumps. That gap is the whole point.
Detect needs roughly 2 pixels across the target, recognise about 8, identify about 13 (a simplified read of the probabilistic Johnson criteria). The prediction line applies those thresholds to the pixels-on-target figure — it estimates probability, it does not prove what a target is.
Never use this calculator to decide whether a target is safe or legal to engage. Positive identification must come from what you can actually observe in the field, not from a modelled pixel count.
Play with it: the resolution comparison slider
Same scene, same target, same field of view — only the sampling density changes. Drag from 256 to 1280 and watch the fox resolve out of the blocks as more cells get to describe it. Nothing about the animal changes; only the number of measurements does.
A deliberately honest illustration: it shows finer sampling, not a real sensor’s noise, tone or processing. It is here to make one idea physical — more pixels means more cells on the same target.
256 thermal resolution explained
A 256×192 detector makes about 49,000 measurements per frame. That is the entry tier, and it is a legitimate resolution class with real strengths, not a failure. Fewer detector elements can help keep the sensor, the processing and the overall system cost down, and a smaller array can allow a more compact unit — genuinely useful for close-range pest work, spotting around a property, or a first thermal where cost is the deciding factor. One myth to sidestep: fewer pixels does not mean bigger pixels. A 256, a 384 and a 640 can all share the same 12 µm pixel pitch; the physical size of a detector depends on both its resolution and its pixel pitch, not on resolution alone.
The compromises are honest ones. With fewer pixels to spread across the scene, small targets at distance are described by very few cells, so recognition and identification fall off sooner, and there is little headroom for digital zoom before the image blocks up. Inside its lane — closer ranges, larger targets, finding rather than identifying — a 256 does its job. Push it to identify a fox across a big paddock and it will run out of pixels. Pixfra’s entry monoculars sit in this class; you can see the range in the monocular category.
384 thermal resolution explained
At 384×288 — about 110,000 pixels — you reach the format that became the workhorse of hunting thermal for good reason. It roughly doubles the 256’s pixel count, which is exactly the jump that takes many real-world targets from “warm blob” to “identifiable animal” at sensible ranges, while keeping size, cost and battery sensible. It tolerates a modest amount of digital zoom, and paired with a well-chosen lens it covers the great majority of Australian pest and hunting scenarios.
Whether to step past it is a genuine buying decision rather than a physics question, and it has its own dedicated lesson — the numbers, the ranges and the honest “is the extra money worth it” answer live here: 384 vs 640: is the upgrade worth it?
640 thermal resolution explained
A 640×512 detector makes about 328,000 measurements — roughly 2.96× the pixels of a 384. That is a lot more spatial information for the designer to spend, and it can go two ways: a wider field of view while still keeping useful detail on a distant animal, or — with a longer lens or finer pitch — more pixels on the target at a comparable field of view. Spent on sampling, it buys more confident identification and far more cropping headroom for digital zoom; spent on width, it buys more scene without giving up the target. What you don’t get for free is both at once from the sensor number alone.
The catch is cost, and one firm caveat: 2.96× the pixels is not 2.96× the range. It is a large increase in sampled spatial information that the optical designer can put toward more target detail or a wider field of view — not a tripling of how far you can “see.” This is the class most of Pixfra’s hero optics target — the Pegasus 2 line and similar sit here; browse the riflescope category for the current 640-class range.
1024-class thermal explained
At 1024×768 — about 786,000 pixels, roughly seven times a 384 — you cross out of mainstream hunting gear and into premium and professional territory. The spatial data set is large, which can mean beautifully detailed images and generous zoom headroom. But it comes with strings: that many pixels demand more processing power, better optics to actually feed sharp light to every pixel, a display good enough to show the detail, and a price to match.
Be clear-eyed about diminishing returns. For ordinary Australian hunting, a well-matched 640 already puts enough pixels on most targets at realistic ranges; a 1024 is not necessary for that work. It earns its keep for open-country scanning, very long ranges, or where you genuinely need both a wide field of view and fine detail at once.
1280 thermal / SXGA explained
1280×1024 — often called SXGA — makes about 1.31 million measurements per frame: exactly four times the pixel count of a 640×512. This is genuinely high detector resolution, and it is not marketing fiction. Detector makers ship it today: Lynred’s Atto1280-02 is a real 1280×1024 microbolometer on a 12 µm pixel pitch, and the maker markets it for both long-range detection and high-quality wide field-of-view imaging — the two things a big, fine detector can do at once.
That dual capability is the point of a top-format sensor. With this many pixels available, a designer can chase reach, or width, or a balance of both, without starving the target of detail. It belongs to high-performance long-range and specialist systems, and it carries the full premium-system requirements — optics, processing, display and cost all have to rise to meet it. Impressive, real, and overkill for most paddock work.
The resolution classes, side by side
| Resolution | Detector pixels | Relative to 384 | Main advantage | Main compromise |
|---|---|---|---|---|
| 256×192 | 49,152 | 0.44× | affordable, compact, light | limited fine detail & zoom |
| 384×288 | 110,592 | 1× | balanced all-rounder | less ID detail than 640 |
| 640×512 | 327,680 | 2.96× | detail, ID, zoom headroom | cost |
| 1024×768 | 786,432 | 7.11× | high spatial detail | optics, processing, price |
| 1280×1024 | 1,310,720 | 11.85× | very high detail & wide FOV | premium system all round |
Read the “relative to 384” column carefully: it is a ratio of pixel counts, not of range or image quality. 640 has 2.96× the pixels of a 384; it does not see 2.96× as far.
Why higher resolution can also give you a wider field of view
Here is the point most retailer copy misses. Resolution isn’t only about reaching further — it is also about seeing wider without going blind. If you have plenty of pixels, a designer can pair a big sensor format with a wider-field lens and still keep enough pixels on a distant target for it to be useful. You get more scene and adequate detail, rather than trading one for the other.
That is exactly why detector makers describe their highest formats as enabling long-range detection and wide field-of-view imaging in the same breath — Lynred says precisely this about its 1280 class. More pixels is optionality: spend them on reach, on width, or on a balance. A low-resolution sensor forces the choice; a high-resolution one lets the designer keep both on the table.
Why the resolution number alone can mislead buyers
Picture two thermals on a shelf. Scenario A is a 640 paired with an undersized lens, a mediocre NETD figure and aggressive processing papering over the gaps. Scenario B is a 384 with a well-matched lens, excellent sensitivity and clean, restrained processing. On the box, A wins on the one number most buyers check. In the paddock, B may well give the better image for a particular job.
Which is actually better? From the resolution number alone, you cannot say — and that is the entire point. A headline pixel count with no lens, no NETD and no word about processing is a single data point pretending to be a verdict. A balanced 384 system routinely outperforms a poorly implemented 640 one. Resolution is powerful, but it only becomes performance when the sensor, lens, sensitivity and processing are working together.
Field note
When you compare two thermals, never compare the resolution numbers in isolation. Line up detector resolution, pixel pitch, lens focal length, NETD and base magnification together. A cheaper unit that wins on one number and loses on three others is not the bargain it looks like.
How much resolution does Australian hunting actually need?
Now the useful question. The honest answer is “it depends on your target and your ranges,” so here is what it depends on, target by target. Treat every range comment as conditional on lens, weather and the specific optic — there are no fixed numbers here, and anyone who gives you one without stating the lens and conditions is guessing.
Rabbits. Small target, so resolution is genuinely valuable — a rabbit at distance is described by very few pixels, and more of them is what keeps it identifiable. This is a case where 640 pulls clearly ahead.
Foxes and feral cats. Small-to-medium targets where identification matters a lot — you want to be sure what you’re looking at. Resolution helps the identification job specifically, which is why fox shooters so often step up to 640.
Pigs. A larger thermal target, so at moderate ranges a lower-resolution sensor can stay perfectly useful — a pig simply fills more pixels than a rabbit at the same distance. Plenty of pig work is done well on a good 384.
Deer. Positive identification is critical, both for a clean result and because you must be certain of your target and everything beyond it. Resolution is valuable here, but it never replaces a proper visual confirmation and staying within the rules that apply where you hunt.
Property and open-country scanning. Finding things over a wide area often rewards a wide field of view more than extreme resolution — you want to sweep ground and catch movement, then close the distance. Here, lens and FOV choices can matter more than chasing the top sensor.
| You are | Priority | Likely resolution class |
|---|---|---|
| First thermal / close work | affordability, simplicity | 256 or 384 |
| General pest control | balanced all-round | 384 or 640 |
| Small targets at distance | identification | 640 and up |
| Open-country scanning | detail plus wide FOV | 640 or 1024, depending on lens |
| Premium enthusiast | maximum detail | 640 or 1024 |
| Specialist / professional | task-dependent | 1024 or 1280 where justified |
Deliberately worded as “likely” and “depending on lens.” These are starting points, not rules — the right class always depends on your lens, your ranges and your targets.
Is 1280 four times better than 640?
1280×1024 does hold exactly four times the detector pixels of 640×512. But four times the pixels is not four times the range, and it is not four times the image quality. The relationship between pixel count and useful performance flattens out — each doubling of resolution buys progressively less extra real-world capability, especially once the rest of the system (lens, NETD, processing, display) becomes the limiting factor. A 1280 can be a superb sensor. It is not “four times the thermal” of a good 640.
Sensor resolution vs sensor size
People use these interchangeably, and they are not the same. Resolution is the number of pixels. Physical sensor size is resolution multiplied by pixel pitch. So a 640×512 built on 12 µm pixels is a physically smaller chip than a 640×512 built on 17 µm pixels — identical pixel count, different physical dimensions.
That matters because the physical array size drives optical design and field of view. Two sensors with the same headline resolution but different pitch will pair with different lenses and produce different fields of view. It is one more reason the resolution number, read alone, can’t tell you how a device will actually behave.
How to read a thermal spec sheet
Put all of this together and you can read a spec sheet the way an engineer does — as a system, in order, rather than hunting for the single biggest number. Run the list top to bottom:
| Read this | Because it tells you |
|---|---|
| 1. Detector resolution | The ceiling on spatial information the sensor can capture |
| 2. Pixel pitch | How big each pixel is — feeds IFOV, physical size and lens choice |
| 3. Lens focal length | How tightly the pixels are projected onto the scene |
| 4. Field of view | How much scene you actually get — the result of format and lens |
| 5. NETD | How faint a temperature difference each pixel can reveal |
| 6. Base magnification | The true optical magnification before any digital zoom |
| 7. Display resolution | The screen — not the sensor; don’t confuse the two |
| 8. Refresh rate | How smoothly moving targets render |
| 9. Image processing | What the software does with the captured data |
| 10. DRI figures | Detect / recognise / identify ranges — note the target and conditions assumed |
Resolution is line one, not the whole sheet. A device is the interaction of all ten, and the best buy is the one that’s balanced across them for your job.
The 384-to-640 maths, in one line
Because this is the upgrade most buyers actually agonise over, it’s worth seeing the arithmetic plainly. A 384×288 is 110,592 detector pixels. A 640×512 is 327,680. Divide them and you get 2.96 — the 640 samples the scene with nearly three times as many measurements.
That is a large jump in spatial information — more detector pixels for the designer to distribute across the scene. What it is not is 2.96× the range, and it is not automatically more pixels on the same target either. With the same pixel pitch and lens, the extra resolution mainly widens the field of view; to turn it into more detail on a distant animal you spend it on a longer lens or a finer pitch. Range and pixels-on-target are governed by lens, pitch and distance as much as by the sensor — so the honest framing is that a 640 gives the designer more to work with, not a guaranteed longer reach.
Want the buying decision, not the physics?
This guide explains what resolution is. If you specifically want the Australian hunting cost-versus-benefit call between these two formats — with the ranges, the products and the “is it worth the money” verdict — that’s a dedicated lesson: 384 vs 640 Thermal Sensor: Is the Upgrade Worth the Money?
Why manufacturers keep pushing resolution higher
Detector resolution has climbed steadily, and it isn’t only marketing. A few real forces are behind it. Fabrication has matured, so makers can reliably produce larger, denser arrays with acceptable yield. Pixel pitches have shrunk from 17 µm toward 12 µm and beyond, letting more pixels fit on a physically sensible chip. Processing power has caught up, so there’s finally the horsepower to handle a million-plus pixels per frame in a handheld device. And demand from premium and professional users rewards the extra capability.
The payoff designers chase is twofold: better identification at range, and the option of a wide field of view without starving the target of detail. That’s why a detector maker like Lynred now ships a 1280×1024 array on a 12 µm pitch and markets it for long-range detection and wide-field imaging alike — concrete evidence that the top formats are real, available technology rather than a spec-sheet flourish. For the buyer, the takeaway isn’t “always buy the newest, biggest sensor.” It’s that more resolution is genuinely useful when the rest of the system is built to use it — and wasted when it isn’t.
Common thermal resolution myths
Buying by the sensor, not just the number
Pixfra builds genuine Australian-stock thermal, backed by C.R. Kennedy service — 640×512 detectors, 12 µm pixels and low-NETD sensitivity in the Pegasus 2 and Cetus lines, matched to sensible lenses so the pixels actually land on target. Balanced systems, not headline numbers.
You now understand the physics of resolution.
Ready for the actual buying decision it leads to?
Continue learning — the Technical Fundamentals cornerstones
Resolution is one node in the system. Here are the lessons it connects to.
The bottom line
Go back to the shelf we started at — the 384 next to the 640, the bigger number looking like the obvious answer. You can see now why that instinct is only half right. The extra pixels are real and worth having; they buy detail, identification and zoom headroom. But they don’t buy range in a straight line, and they never work alone. A 640 behind the wrong lens, starved of sensitivity, can lose to a well-built 384.
The question was never “how many pixels?” It was always whether the whole system — sensor, lens, pitch, NETD and processing — puts enough useful pixels on the target for the job you’re asking of it.
Thermal resolution tells you how many measurements the detector makes. What wins in the field is how many of them land on the target.
Thermal Academy
Understand that, and you can walk past the biggest number on the box and buy the thermal that actually sees what you need to see. See more. Know more. Hunt smarter.
Thermal resolution FAQ
What is thermal resolution?
Thermal resolution is the number of individual infrared detector elements in the sensor, written as width × height — for example 640×512. Each element takes its own temperature reading every frame, so resolution sets how many measurements of the scene the sensor can make, and therefore the maximum spatial detail it can ever capture.
Is 256×192 thermal resolution good?
It’s a legitimate entry class with about 49,000 pixels. It’s well suited to closer-range work, larger targets and buyers prioritising cost, size and battery life. Its limits show with small targets at distance and with digital zoom, where it runs short of pixels sooner than a 384 or 640.
Is 384×288 enough for hunting?
For a great deal of Australian pest and hunting work, yes. With about 110,000 pixels and a well-matched lens, a 384 identifies most targets at sensible ranges. Whether to step up to 640 depends on your targets and distances — small targets far out benefit most from the extra resolution.
Is 640×512 thermal worth it?
Often, yes — it holds nearly three times the pixels of a 384, giving the designer more spatial information to spend on either a wider field of view or more detail on target, plus much more digital-zoom headroom. Just don’t expect proportionally more range: 2.96× the pixels is not 2.96× the reach, and with the same lens and pitch it mainly widens the view rather than adding pixels on the same target.
Is 1280 thermal much better than 640?
1280×1024 holds exactly four times the detector pixels of 640×512, so it can produce very high detail and wide fields of view. But four times the pixels is not four times the range or image quality — returns diminish, and lens, sensitivity and processing become the limits. It’s excellent, and overkill for most paddock work.
Does higher thermal resolution see further?
It depends what “see” means. Detection (something warm is there) needs very few pixels and barely improves with resolution. Recognition and identification need many more pixels; higher resolution can extend those distances, but only when the extra pixels are spent on sampling the target rather than on a wider field of view.
What is the best thermal resolution?
There isn’t a single best. The right class depends on your target size, your ranges and your lens. A balanced 640 suits most serious hunting; 384 covers a lot of general work; 1024 and 1280 are for specialist or premium needs. The best thermal is the one balanced across sensor, lens, NETD and processing for your job.
What does 640×512 mean?
It means the detector is 640 pixels wide and 512 pixels tall, so 640 multiplied by 512 gives 327,680 individual infrared detectors making a reading each frame.
Is display resolution the same as sensor resolution?
No. Display resolution is the screen you look at through the eyepiece; sensor (detector) resolution is the real infrared sampling. A large display does not turn a small detector into a high-resolution sensor — it just shows the detector’s data on more glass.
Does digital zoom increase thermal resolution?
No. Digital zoom crops into the pixels you already captured and enlarges them. It creates no new detector pixels and no new detail. A higher-resolution sensor simply gives you more native pixels to enlarge before the image turns blocky.
Which matters more: resolution or NETD?
Neither alone — they’re different halves of image quality. Resolution is how finely the scene is divided; NETD is how faint a temperature difference each division can reveal. A high-resolution sensor with poor sensitivity can look sharp but flat; a sensitive sensor with lower resolution can look clean but soft.
Which matters more: resolution or lens size?
They work together and can’t be ranked in the abstract. The lens decides how tightly the sensor’s pixels are projected onto the scene, so the same 640 behind a 25 mm and a 50 mm lens puts very different numbers of pixels on a distant target.
What is IFOV?
IFOV, or instantaneous field of view, is the angular slice of the scene that a single detector pixel covers. For small angles it’s roughly the pixel pitch divided by the focal length. A smaller IFOV means each pixel looks at a smaller patch, so more pixels land on a target and more detail survives.
What is the difference between pixel pitch and resolution?
Resolution is how many pixels the sensor has; pixel pitch is how big each pixel is (for example 12 µm or 17 µm). Two sensors can share the same resolution but different pitches, giving different physical chip sizes and different optical behaviour.
Why can a 384 thermal sometimes look better than a 640?
Because resolution is only one part of the system. A 384 with a well-matched lens, excellent NETD and good processing can produce a better image for a given job than a 640 hampered by a poor lens, weak sensitivity or heavy-handed processing. The balanced system wins.
Educational guide only. Always positively identify your target and everything beyond it before acting, and follow the firearms and hunting rules that apply where you hunt. Pixel counts are exact arithmetic; IFOV and pixels-on-target figures are illustrative, drawn from standard optical relationships and a simplified reading of the Johnson criteria (1958, 50% probability: detection 1.0, recognition 4.0, identification 6.4 line pairs across the target). Detector-class examples reference published manufacturer documentation, including Lynred’s 12 µm Atto detector range (Atto1280-02 at 1280×1024) and Teledyne FLIR engineering material, which treat resolution, sensitivity, IFOV and field of view as separate interacting specifications.
