Spatial feature manipulation in remote sensing

Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image.

One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail.

Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching or color balancing, to improve the visual appearance of the image.

Overall, spatial feature manipulation is an important tool in remote sensing, as it allows analysts to extract valuable information from complex and noisy data sets, and to present that information in a clear and meaningful way

Spatial feature manipulation in remote sensing refers to the process of manipulating the spatial characteristics of an image or data in order to better visualize or analyze specific features or patterns. This can involve a range of techniques, such as resampling or resizing the image to enhance resolution or zoom in on specific features, cropping the image to focus on a particular area of interest, or applying spatial filters to highlight specific patterns or characteristics. By manipulating spatial features in remote sensing data, researchers can more effectively identify and analyze patterns and trends in the data, providing valuable insights and information for a variety of applications.

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Optical Sensors in Remote Sensing

1. What Are Optical Sensors? Optical sensors are remote sensing instruments that detect solar radiation reflected or emitted from the Earth's surface in specific portions of the electromagnetic spectrum (EMS) . They mainly work in: Visible region (0.4–0.7 µm) Near-Infrared – NIR (0.7–1.3 µm) Shortwave Infrared – SWIR (1.3–3.0 µm) Thermal Infrared – TIR (8–14 µm) — emitted energy, not reflected Optical sensors capture spectral signatures of surface features. Each object reflects/absorbs energy differently, creating a unique spectral response pattern . a) Electromagnetic Spectrum (EMS) The continuous range of wavelengths. Optical sensing uses solar reflective bands and sometimes thermal bands . b) Spectral Signature The unique pattern of reflectance or absorbance of an object across wavelengths. Example: Vegetation reflects strongly in NIR Water absorbs strongly in NIR and SWIR (appears dark) c) Radiance and Reflectance Radi...

Radar Sensors in Remote Sensing

Radar sensors are active remote sensing instruments that use microwave radiation to detect and measure Earth's surface features. They transmit their own energy (radio waves) toward the Earth and record the backscattered signal that returns to the sensor. Since they do not depend on sunlight, radar systems can collect data: day or night through clouds, fog, smoke, and rain in all weather conditions This makes radar extremely useful for Earth observation. 1. Active Sensor A radar sensor produces and transmits its own microwaves. This is different from optical and thermal sensors, which depend on sunlight or emitted heat. 2. Microwave Region Radar operates in the microwave region of the electromagnetic spectrum , typically from 1 mm to 1 m wavelength. Common radar frequency bands: P-band (70 cm) L-band (23 cm) S-band (9 cm) C-band (5.6 cm) X-band (3 cm) Each band penetrates and interacts with surfaces differently: Lo...

Thermal Sensors in Remote Sensing

Thermal sensors are remote sensing instruments that detect naturally emitted thermal infrared (TIR) radiation from the Earth's surface. Unlike optical sensors (which detect reflected sunlight), thermal sensors measure heat energy emitted by objects because of their temperature. They work mainly in the Thermal Infrared region (8–14 µm) of the electromagnetic spectrum. 1. Thermal Infrared Radiation All objects above 0 Kelvin (absolute zero) emit electromagnetic radiation. This is explained by Planck's Radiation Law . For Earth's surface temperature range (about 250–330 K), the peak emitted radiation occurs in the 8–14 µm thermal window . Thus, thermal sensors detect emitted energy , not reflected sunlight. 2. Emissivity Emissivity is the efficiency with which a material emits thermal radiation. Values range from 0 to 1 : Water, vegetation → high emissivity (0.95–0.99) Bare soil → medium (0.85–0.95) Metals → low (0.1–0.3) E...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

LiDAR in Remote Sensing

LiDAR (Light Detection and Ranging) is an active remote sensing technology that uses laser pulses to measure distances to the Earth's surface and create high-resolution 3D maps . LiDAR sensors emit short pulses of laser light (usually in the near-infrared range) and measure the time it takes for the pulse to return after hitting an object. Because LiDAR measures distance very precisely, it is excellent for mapping: terrain vegetation height buildings forests coastlines flood plains ✅ 1. Active Sensor LiDAR sends its own laser energy, unlike passive sensors that rely on sunlight. ✅ 2. Laser Pulse LiDAR emits thousands of pulses per second (even millions). Wavelengths commonly used: Near-Infrared (NIR) → land and vegetation mapping Green (532 nm) → water/ bathymetry (penetrates shallow water) ✅ 3. Time of Flight (TOF) The sensor measures the time taken for the laser to travel: from the sensor → to the sur...

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