Digital Image Processing of Remote-Sensed Data
Digital Image Transformation
Image transformations are operations similar in concept to those for image enhancement. However, unlike image enhancement operations, which are normally applied only to a single channel of data at a time, image transformations usually involve the combined processing of data from multiple spectral bands. Consequently, new resultant transformed images generated from two or more sources highlight particular features or properties of interest better than the original input images. Hence, the transformed image may have properties that make it more suited to a particular purpose than the original input images.
Image Transformation Techniques
Image transformation techniques in digital image processing are methods that modify or derive new image data from the original bands to enhance specific features, compress data, or support analysis such as classification and interpretation. These transformations are especially important in remote sensing.
Band Ratioing
Band ratio is an image transformation technique that is applied to enhance the contrast between features by dividing digital numbers (DNs) for pixels in one image band by the DN of pixels in the other image band. Band ratioing involves dividing the digital number (DN) values of corresponding pixels in two different spectral bands. This operation creates a new "ratioed image" where the pixel values represent the ratio of the reflectance values in those two bands. The main purpose of band ratioing is to enhance spectral differences between features in an image, while minimizing the effect of variations in scene illumination and topography.
Vegetative Indices
Vegetation indices (VIs) are one of the most widely used image transformation techniques in digital image processing, especially for remote sensing applications in agriculture, forestry, and environmental monitoring. These indices use specific spectral band combinations, typically from multispectral or hyperspectral imagery, to highlight vegetation characteristics such as health, vigor, biomass, or moisture content.
Image Fusion
Image fusion is the process of combining relevant information from two or more remotely sensed images of a scene into a highly informative single image. The primary reason image fusion has gained prominence in remote sensing applications is based on the fact that the remote sensing instruments have design or observational constraints. Therefore, a single image is not sufficient for visual or machine-level information analysis. For example, infrared images capture the thermal radiation of objects that is uninfluenced by changes in illumination, weather, and other disturbances.
Color Space Transformations
Color space transformations play a significant role in image segmentation, as they can help to highlight specific features or properties of an image that might be more prominent or easier to segment in a different color space. Image segmentation relies on the color features of image pixels to group pixels with similar properties together. Different color spaces represent color information in distinct ways, and choosing the appropriate color space can significantly impact the effectiveness of segmentation algorithms. Common color spaces and their applications include:
Principal Component Analysis
Principal component analysis (PCA) is a statistical technique used to transform a set of possibly correlated spectral bands into a smaller set of uncorrelated variables called principal components (PCs). It is widely used in remote sensing and multispectral/hyperspectral image analysis for data compression, feature extraction, and sometimes image fusion. PCA is used in agriculture to analyze and simplify multispectral or hyperspectral remote sensing data, making it easier to monitor, classify, and assess crop and soil conditions by emphasizing variation and reducing redundancy
Tasseled Cap Transformation
The tasseled cap transformation (TCT) is a technique used in remote sensing to transform raw spectral data from multiple bands into a smaller set of meaningful components using linear combinations of the original image bands, similar to principal components analysis (PCA). The resulting components have specific meanings, commonly including brightness (representing overall image brightness related to features like soil and bare ground), greenness (an index of photosynthetically active vegetation), and wetness (indicating soil or surface moisture, or "yellowness").
Texture and Frequency-Based Transforms
In digital image processing, texture and frequency-based segmentation techniques use spatial variation in pixel intensity patterns (not just spectral values) to differentiate features. These techniques are powerful for identifying repeating structures, surface roughness, or anomalies—essential for agricultural analysis where texture can indicate crop rows, weed patches, soil type, or disease spread.
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