Mastering Supervised Classification Methods

Mastering Supervised Classification Methods

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Table of Contents

1. Introduction
2. Visual Interpretation Method
3. Digital Interpretation Method
4. Steps in Digital Interpretation Method
   • Field Reconnaissance
   • Development of Classification Scheme
   • Image Enhancement
   • Selection of Classification Algorithm
   • Incorporation of Ancillary Data
   • Checking the Accuracy of Results
   • Iterative Procedure for Improved Accuracy
5. Pixel-Based Classification
6. Types of Classification Methods
   • Supervised Method
   • Unsupervised Method
   • Object-Based Classification
   • Artificial Neural Network
   • Deep Learning Techniques
7. Steps in Supervised Classification
   • Training Sets Identification
   • Characteristics of Training Samples
   • Selection of Sample Size and Scheme
   • Guidelines for Selecting Training Data
   • Importance of Homogeneous Training Data
   • Determination of Sample Range
8. Sample Selection Process
9. Histogram Analysis for Homogeneity
10. Importance of Ground Truth Data
11. Classification Methods
    • Parallel Pipette Classifier
    • Minimum Distance to Means Classifier
    • Maximum Likelihood Classifier
12. Pros and Cons of Supervised Classification Techniques
13. Conclusion

Article

Introduction

In Lecture 47, we will be discussing supervised classification methods for land use and land cover classification from satellite images. There are two broad methods for image classification: visual interpretation and digital interpretation. In this lecture, we will focus on the digital interpretation method, which involves using digital images and software to carry out the classification. The main objective of digital image classification is to classify all the pixels in an image into different homogeneous classes based on their digital number (DN) values and reflectance characteristics.

Visual Interpretation Method

Visual interpretation is a method of image classification where the interpreter visually analyzes the satellite image and manually assigns different classes to different areas based on their visual characteristics. This method does not require any software or algorithm and relies solely on the interpreter's expertise. However, it is subjective and time-consuming, as it involves analyzing each pixel individually.

Digital Interpretation Method

The digital interpretation method, on the other HAND, relies on the DN values of the pixels in a digital image to classify them into different classes. The basic process involves selecting a classification algorithm, such as parallel pipette, minimum distance to means, or maximum likelihood, and applying it to the DN values of the pixels. The algorithm uses mathematical transformations and rules to classify the pixels into specific classes based on their similarity.

Steps in Digital Interpretation Method

The digital interpretation method follows a set of steps to ensure accurate classification results. The first step is field reconnaissance, where the interpreter gathers information about the area's features, topography, size, and orientation. This information helps in selecting the appropriate classification scheme.

The next step is the development of a classification scheme, which determines the number and types of classes to be identified in the image. Anderson's classification scheme, which includes level one to level four classifications, is commonly used. The level of classification depends on the required level of Detail.

Image enhancement is another important step in the digital interpretation method. It involves enhancing the image's quality and Clarity using techniques like contrast adjustment, histogram equalization, and filtering. The enhanced image provides a clearer representation of the different features in the area.

After image enhancement, a proper classification algorithm is selected based on the specific requirements of the classification task. Different algorithms, such as parallel pipette, minimum distance to means, and maximum likelihood, have their own advantages and limitations. The choice of algorithm depends on factors like speed, accuracy, and class order dependency.

Incorporating ancillary data, such as a digital elevation model or NDVI values, can improve the accuracy of the classification. Ancillary data provides additional information that aids in distinguishing between different classes.

Once the classification is done, it is crucial to check the accuracy of the output. This involves comparing the classified map with ground reference data or known features in the area. The accuracy assessment helps in evaluating the reliability and precision of the classification results.

If necessary, the procedure is iteratively repeated to improve the accuracy of the classification. This iterative process involves refining the training sets, adjusting the classification scheme, or modifying the algorithm parameters.

Pixel-Based Classification

In digital image classification, the aim is to classify each pixel in the image into homogeneous classes. The number of classes can vary depending on the diversity of features within the area. It is important to accurately separate similar classes and avoid classifying pixels into incorrect categories.

The classification process considers the DN values of each pixel and assigns them to the corresponding classes based on predefined rules and thresholds. The distribution of DN values in the dataset plays a crucial role in determining the spectral similarity or dissimilarity between classes. It is essential to select training sets that represent the homogeneity of the classes and cover the entire range of DN values.

Types of Classification Methods

Apart from supervised classification, there are other techniques available in the literature, such as unsupervised classification, object-based classification, artificial neural network, and deep learning techniques. These methods offer more advanced and automated ways of classifying images. However, for this lecture, we will focus on supervised and unsupervised classification techniques.

Steps in Supervised Classification

Supervised classification involves human intervention and requires a significant amount of ground truth data for accurate classification. The process of supervised classification can be divided into several steps.

The first step is the identification of training sets, which are representative samples of each class. The training sets should be homogeneous and well-distributed across the image. They need to capture the spectral characteristics of the classes they represent.

The characteristics of the training samples, such as the DN values and their distribution, are crucial in the classification process. The DN values of the training samples are used as input to the classification algorithm. The range of DN values within each class helps in defining the boundaries for classification.

Guidelines for selecting training data suggest choosing areas with uniform tones and spectral similarity. The training samples should cover the entire range of DN values and have sufficient representation of each class. Homogeneous and well-distributed training samples provide the foundation for accurate classification.

The sample size and sampling scheme are important considerations in supervised classification. The literature offers different criteria, such as a minimum of 30 training samples per class or 5 to 10 samples per category. The selection of sample size and scheme depends on various factors, including the size of the area, class homogeneity, and the number of classes.

The selection process involves visually identifying areas on the image that represent each class. Topographical maps or aerial photographs can aid in this process. The selected training samples should be homogeneous, representative, and adequately cover the classes they represent.

Histogram analysis is an effective technique for assessing the homogeneity of training samples. A well-distributed and distinct histogram indicates the separability and spectral similarity between classes. Histogram analysis helps in refining the training samples and ensuring accurate classification.

Classification Methods

Several classification methods can be used in supervised classification. Three popular techniques are the parallel pipette classifier, minimum distance to means classifier, and maximum likelihood classifier.

The parallel pipette classifier involves creating a three-dimensional box using the mean, minimum, and maximum values of the training samples within the range of DN values. The box represents the acceptable range for each class. Each pixel is classified into the class that falls within its DN value range.

In the minimum distance to means classifier, the Euclidean distance between the DN values of a pixel and the mean values of the training samples is calculated. The pixel is assigned to the class with the minimum distance. This method is faster than the parallel pipette classifier but does not consider class order dependency.

The maximum likelihood classifier is based on probability functions. It calculates the likelihood of a pixel belonging to a particular class based on the probability distribution of the training samples. The pixel is classified into the class with the highest probability. This method is more time-consuming but theoretically provides the most accurate results.

Pros and Cons of Supervised Classification Techniques

Each supervised classification technique has its pros and cons. The parallel pipette classifier is faster but dependent on class order. The minimum distance to means classifier is intermediate in speed and independent of class order. The maximum likelihood classifier is the slowest but theoretically accurate.

In conclusion, supervised classification methods play a crucial role in land use and land cover classification from satellite images. They require a careful selection of training samples, the application of appropriate classification algorithms, and the assessment of classification accuracy. These methods provide a systematic approach to classifying and analyzing satellite images, enabling better understanding and management of land resources.