Here are the general steps for image preprocessing before computing band indices:
Image acquisition: Obtain the raw image data, which may be in different formats such as TIFF, JPEG, or ENVI.
Radiometric calibration: Convert the raw image data into units of radiance or reflectance to correct for any variations in the sensor response due to atmospheric or other environmental factors.
Image registration: Align the different image bands or scenes to a common spatial reference system to ensure that pixel values from the same location represent the same physical feature.
Image resampling: Resample the image to a consistent spatial resolution to ensure that the pixel sizes are consistent across all bands.
Image enhancement: Apply image enhancement techniques to improve the visual quality of the image and to highlight features of interest. This may include techniques such as contrast stretching, histogram equalization, or filtering.
Noise reduction: Apply noise reduction techniques to remove any noise or unwanted artifacts from the image data. This may include techniques such as median filtering or wavelet denoising.
Image segmentation: Segment the image into meaningful regions or objects based on their spectral, spatial, and/or temporal characteristics. This may be useful for feature extraction or object-based analysis.
Image normalization: Normalize the image to eliminate any systematic variations in the image data due to illumination differences or other factors.
After these preprocessing steps, the band indices can be computed using mathematical formulas that combine the spectral information from different bands to extract specific features or properties of interest, such as vegetation health or water quality.
Before computing band indices, it is essential to preprocess the image to ensure accurate and consistent results. Here are some general steps for image preprocessing:
Radiometric Correction: Radiometric correction is the process of removing noise and variations in the image due to atmospheric conditions, sensor noise, or other factors. This can be done using methods like dark subtraction, flat-field correction, or atmospheric correction.
Geometric Correction: Geometric correction is the process of correcting geometric distortions in the image due to sensor orientation, terrain relief, or other factors. This can be done using methods like orthorectification, registration, or mosaicking.
Image Enhancement: Image enhancement techniques can be used to improve the contrast, brightness, and color balance of the image. This can be done using methods like histogram equalization, contrast stretching, or sharpening.
Noise Reduction: Noise reduction techniques can be used to remove random variations or patterns in the image that can affect the accuracy of band indices. This can be done using methods like filtering, smoothing, or averaging.
Image Segmentation: Image segmentation is the process of dividing the image into regions or objects based on their characteristics, such as color, texture, or shape. This can be useful for identifying regions of interest for computing band indices.
Preprocessing steps, such as compression, aim to prepare data and facilitate processing activities. Information supply chains in the big data environment that transform data from its source format into a variety of consumer formats for analysis and use are also covered in preprocessing activities, such as format conversion. Depending on the state of the data, processing can be classified as stream processing (eg, filtering, annotation) or batch processing (eg, cleaning, combining, and iteration). For further processing, information extraction, data integration, in-memory processing, and data assimilation activities can be used, depending on system requirements. Suhel Sen