What is the classification of images? Describe the procedures and techniques used in supervised image categorization.
Spectral Signature: The spectral signature of an object refers to its unique pattern of reflection, absorption, and transmission of electromagnetic radiation across various wavelengths of the electromagnetic spectrum. Different materials exhibit distinct spectral signatures due to their inherent proRead more
Spectral Signature:
The spectral signature of an object refers to its unique pattern of reflection, absorption, and transmission of electromagnetic radiation across various wavelengths of the electromagnetic spectrum. Different materials exhibit distinct spectral signatures due to their inherent properties, making them identifiable and distinguishable through remote sensing technologies. Spectral signatures are crucial in analyzing and interpreting satellite or aerial imagery.
Spectral Signature of Vegetation:
Vegetation has a characteristic spectral signature primarily influenced by the absorption and reflection properties of chlorophyll, carotenoids, and other pigments. Here's a description accompanied by a labeled diagram:
Diagram of Spectral Signature of Vegetation:
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Visible Range (400 – 700 nm):
- In the visible range, chlorophyll strongly absorbs light in the blue (around 450 nm) and red (around 660 nm) wavelengths while reflecting green light (around 550 nm). This results in the characteristic green color of healthy vegetation in satellite imagery.
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Near-Infrared (NIR) Range (700 – 1400 nm):
- Vegetation strongly reflects near-infrared radiation due to the cellular structure of leaves. Healthy vegetation exhibits high reflectance in this range, creating a distinctive peak in the spectral signature. This characteristic is exploited in various vegetation indices like the Normalized Difference Vegetation Index (NDVI).
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Red Edge (700 – 750 nm):
- The red edge region, located between the red and NIR ranges, is sensitive to chlorophyll content. Changes in chlorophyll concentration affect the shape and position of the red edge, providing information about the health and vigor of vegetation.
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Shortwave Infrared (SWIR) Range (1400 – 3000 nm):
- In the SWIR range, vegetation shows increased absorption due to water content in plant tissues. This absorption is influenced by the amount of water in leaves, providing information about vegetation moisture content.
Spectral Signature of Water:
Water bodies exhibit unique spectral signatures primarily influenced by their optical properties. Here's a description accompanied by a labeled diagram:
Diagram of Spectral Signature of Water:
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Visible Range (400 – 700 nm):
- Water absorbs light in the blue part of the spectrum (around 450 nm) and to a lesser extent in the red part. This absorption causes water bodies to appear dark in the blue and red color channels of satellite imagery.
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Near-Infrared (NIR) Range (700 – 1400 nm):
- Water bodies reflect near-infrared radiation to a limited extent. The reflectance in the NIR range is lower compared to that of vegetation, contributing to the dark appearance of water in remote sensing data.
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Shortwave Infrared (SWIR) Range (1400 – 3000 nm):
- In the SWIR range, water absorption increases, particularly due to the presence of water molecules. This increased absorption is useful for distinguishing water bodies from other features in satellite imagery.
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Thermal Infrared Range (3000 nm and beyond):
- In the thermal infrared range, water exhibits strong absorption due to its unique thermal properties. This absorption can be detected by sensors sensitive to thermal radiation, providing additional information about water temperatures.
Understanding the spectral signatures of vegetation and water is fundamental in remote sensing applications, allowing for the identification, classification, and monitoring of these features across landscapes. Advanced satellite sensors and spectral analysis techniques contribute to a more nuanced interpretation of spectral signatures, enabling comprehensive studies in agriculture, environmental monitoring, and water resource management.
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Image classification is a process in remote sensing and computer vision that involves categorizing pixels or regions within an image into predefined classes or land cover types. The goal is to assign each pixel in an image to a specific category based on its spectral characteristics. Supervised imagRead more
Image classification is a process in remote sensing and computer vision that involves categorizing pixels or regions within an image into predefined classes or land cover types. The goal is to assign each pixel in an image to a specific category based on its spectral characteristics. Supervised image classification relies on training samples with known class labels to teach a computer algorithm to identify and classify pixels in the image.
Methods of Supervised Image Classification:
Maximum Likelihood Classification:
Support Vector Machines (SVM):
Random Forest:
Neural Networks (Deep Learning):
Steps of Supervised Image Classification:
Data Collection:
Data Preprocessing:
Training Sample Selection:
Feature Extraction:
Training the Classifier:
Image Classification:
Accuracy Assessment:
Post-Classification Processing:
Supervised image classification is a powerful tool for extracting valuable information from remotely sensed imagery. It is widely used in applications such as land cover mapping, agricultural monitoring, environmental assessment, and urban planning. The effectiveness of the classification process depends on careful data preparation, feature extraction, and the selection of an appropriate classification algorithm.
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