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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Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...

Types of Remote Sensing

Remote Sensing means collecting information about the Earth's surface without touching it , usually using satellites, aircraft, or drones . There are different types of remote sensing based on the energy source and the wavelength region used. 🛰️ 1. Active Remote Sensing 📘 Concept: In active remote sensing , the sensor sends out its own energy (like a signal or pulse) to the Earth's surface. The sensor then records the reflected or backscattered energy that comes back from the surface. ⚙️ Key Terminology: Transmitter: sends energy (like a radar pulse or laser beam). Receiver: detects the energy that bounces back. Backscatter: energy that is reflected back to the sensor. 📊 Examples of Active Sensors: RADAR (Radio Detection and Ranging): Uses microwave signals to detect surface roughness, soil moisture, or ocean waves. LiDAR (Light Detection and Ranging): Uses laser light (near-infrared) to measure elevation, vegetation...

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...

Resolution of Sensors in Remote Sensing

Spatial Resolution 🗺️ Definition : The smallest size of an object on the ground that a sensor can detect. Measured as : The size of a pixel on the ground (in meters). Example : Landsat → 30 m (each pixel = 30 × 30 m on Earth). WorldView-3 → 0.31 m (very detailed, you can see cars). Fact : Higher spatial resolution = finer details, but smaller coverage. Spectral Resolution 🌈 Definition : The ability of a sensor to capture information in different parts (bands) of the electromagnetic spectrum . Measured as : The number and width of spectral bands. Types : Panchromatic (1 broad band, e.g., black & white image). Multispectral (several broad bands, e.g., Landsat with 7–13 bands). Hyperspectral (hundreds of very narrow bands, e.g., AVIRIS). Fact : Higher spectral resolution = better identification of materials (e.g., minerals, vegetation types). Radiometric Resolution 📊 Definition : The ability of a sensor to ...

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...

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