Image Manipulation: Band Arithmetic and Thresholds

Remote sensing is a dynamic field that allows researchers and professionals to collect and analyze data from a distance, often from satellites or aircraft. One of the most challenging aspects of remote sensing lies in manipulating and interpreting acquired images. This module explores two fundamental image manipulation techniques: band arithmetic and thresholds.

Band Arithmetic: The Math Behind the Pixels

What is Band Arithmetic?

Band arithmetic is a pixel-based operation that manipulates the spectral values in individual bands of a multiband image. It involves mathematical operations such as addition, subtraction, multiplication, and division. The purpose is to highlight specific features or differences that are otherwise not easily visible in a single band or in a composite RGB image.

Why Use Band Arithmetic?

Feature Enhancement: Highlight certain features such as water bodies, vegetation, or urban areas.
Change Detection: Compare images from different times to detect changes in land use, vegetation, etc.
Noise Reduction: Suppressing random noise in the image by balancing the spectral values.

Common Formulas

Here are some commonly used band arithmetic formulas:

Normalized Difference Vegetation Index (NDVI) = $LaTeX: \frac{(NIR-RED)}{(NIR+RED)}$
Soil Adjusted Vegetation Index (SAVI) = $LaTeX: \frac{(NIR−RED)}{(NIR+RED+L)}×(1+L)$
Water Index = $LaTeX: \frac{(GREEN−NIR)}{(GREEN+NIR)}$

Practical Applications

Band arithmetic finds diverse applications:

Agriculture: Monitoring crop health and soil quality.
Environmental Science: Studying deforestation and water quality.
Urban Planning: Assessing land use and infrastructure development.

Thresholds: Making Sense of Spectral Values

What are Thresholds?

Thresholding is the process of classifying pixels based on their spectral values. It involves setting a specific value or range of values as a "cut-off," and classifying the pixels that fall above or below this range.

Types of Thresholding

Global Thresholding: A single value is used for the entire image.
Local Thresholding: Different threshold values for different regions of the image.
Dynamic Thresholding: Thresholds are set based on statistical or machine learning methods.

Practical Applications of Thresholding

Vegetation Mapping: Identifying areas of dense vegetation based on specific spectral values.
Water Body Detection: Classifying water bodies based on their reflectance properties.
Urban Area Identification: Isolating built-up areas from the natural landscape.

Combining Band Arithmetic and Thresholding

Often, band arithmetic and thresholding are used in tandem for more accurate image analysis. For instance, you could first calculate the NDVI of an area and then apply a threshold to separate vegetation from non-vegetation areas. This approach has benefited various sectors, including wildlife conservation, disaster management, and climate change studies.