Questions and Answers
What portions of the electromagnetic spectrum are referred to in remote sensing?
What is a single-band image?
An image that contains only one band.
What is a multi-band image?
An image that contains multiple bands.
What is the purpose of image classification?
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The basic process for image classification includes three major steps: define the spectral characteristics, assign objects to classes, and assess the _____ of the classification.
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What does the spectral signature of a feature represent?
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What are the two approaches to define signatures for a feature type?
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What does the supervised approach to define signatures involve?
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Study Notes
Image Classification Overview
- Images are generated from sensors, capturing data across the electromagnetic spectrum.
- Each sensor is sensitive to specific portions of this spectrum, called bands.
- Photographs from devices, like smartphones, typically generate three raster datasets: red, green, and blue bands.
Single-Band Image
- Defined as an image containing only one band.
- Represents a single reflectance value for each pixel.
- Commonly produced by black and white or grayscale cameras, which utilize a single sensor.
- Typically displayed in grayscale to convey signal strength.
- Can be displayed in separate colors, but this may create confusion with multi-band images.
Multi-Band Image
- Comprises multiple bands, each capturing different reflectance values at every pixel.
- Standard in satellite imagery, where bands cover specific ranges of the electromagnetic spectrum.
Purpose of Classification
- Image classification groups pixels in a multi-band image into classes based on their reflectance values.
- The aim is to identify various feature types (e.g., forests, water bodies) from multi-band rasters.
- Classification results can be utilized to produce thematic maps.
Basic Process of Classification
- Define the spectral characteristics (signature) of feature types, outlining unique combinations from the bands.
- Assign pixels to classes based on their signatures compared to defined class signatures.
- Assess the quality of the classification, evaluating accuracy.
Signature in Image Classification
- A spectral signature combines spectral reflectance values from multiple data layers.
- Different feature types (e.g., forest vs. water) show distinct signatures, while similar signatures are found within the same class.
- Class signatures may be derived from a representative sample, which may not match exactly due to variability.
- Signatures are described by mean and standard deviation values.
- Feature space utilizes bands as axes, helping to plot pixels and classes based on their signatures.
Defining Signatures
- Two approaches exist for defining signatures: supervised and unsupervised.
Supervised Approach
- Involves two main steps:
- Identify areas where the specific feature type occurs by manually outlining these areas.
- Derive combinations of reflectance values from the image bands in the outlined areas using statistical methods.
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Description
This quiz covers key concepts in image classification, specifically focusing on remote sensing and the electromagnetic spectrum. Learn about sensor devices and how they capture images across different bands, such as red, green, and blue. Enhance your understanding of image datasets in remote sensing.
