Remote sensing is a technology that has been used for decades to collect information about the Earth’s surface using sensors mounted on satellites, airplanes, or drones. The data collected through remote sensing is used for a variety of applications, including agriculture, forestry, natural resource management, urban planning, and environmental monitoring. One of the primary applications of remote sensing is image classification, which involves categorizing different land covers or features in an image. This article will discuss the most common image classification techniques used in remote sensing.
Image classification is a process that involves grouping pixels in an image into different classes or categories based on their spectral characteristics. Spectral characteristics refer to the way the pixel responds to different wavelengths of electromagnetic radiation, such as visible light, near-infrared, and thermal radiation. The main goal of image classification is to identify different land covers or features present in an image and to map them accurately.
There are several image classification techniques commonly used in remote sensing. These techniques can be broadly classified into supervised and unsupervised classification methods. Let’s discuss each of these techniques in detail.
Supervised Classification:
Supervised classification is a technique that involves training a classifier using a set of labeled data. Labeled data refers to an image in which each pixel is assigned a class label based on ground truth information, such as land cover maps or field surveys. The classifier is trained to recognize patterns in the labeled data and can then be applied to classify new images. The most common supervised classification methods used in remote sensing are Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) classifier.
The MLC classifier assumes that the spectral values of different classes follow a normal distribution, and the classifier computes the probability of each pixel belonging to different classes based on the spectral values. The pixel is then assigned to the class with the highest probability. The SVM classifier works by finding the optimal hyperplane that separates different classes based on their spectral values. The classifier is trained to maximize the margin between the hyperplane and the nearest data points
Unsupervised Classification:
Unsupervised classification is a technique that involves clustering pixels based on their spectral similarity without prior knowledge of the classes present in the image. The user needs to identify the number of clusters required to represent the data and interpret the resulting classes. The most common unsupervised classification methods used in remote sensing are the K-means clustering algorithm and the ISODATA clustering algorithm.
The K-means clustering algorithm works by randomly selecting a set of pixels as initial cluster centers and then iteratively reassigning pixels to the closest cluster center based on their spectral values. The algorithm stops when the clusters do not change significantly between iterations. The ISODATA clustering algorithm is an extension of the K-means algorithm, which involves merging and splitting clusters based on statistical criteria until a desired number of clusters is obtained.
Object-based Classification:
Object-based classification is a technique that uses a combination of spectral, spatial, and contextual information to classify objects rather than individual pixels. Things can be defined based on size, shape, texture, and other features. Object-based classification is beneficial for mapping complex landscapes with multiple land covers and for mapping features that are not easily distinguishable at the pixel level, such as roads, buildings, and water bodies.
The object-based classification process involves segmenting an image into a set of homogeneous regions based on their spectral and spatial characteristics. The regions are then classified based on their attributes, such as size, shape, texture, and contextual information. The most common object-based classification methods used in remote sensing are the Multi-resolution Segmentation (MRS) algorithm and the rule-based classification approach.
Deep Learning Classification:
Deep learning classification is a state-of-the-art technique that has shown significant promise in remote sensing image classification. It involves training a neural network to automatically learn features from the input data and use them to make accurate predictions. The neural network is typically composed of multiple layers of interconnected nodes, each of which performs a nonlinear operation on the input data. The output of each layer is passed as input to the next layer, allowing the network to learn increasingly complex representations of the input data.
The deep learning approach is particularly useful in remote sensing because it can learn features that are difficult to capture using traditional hand-crafted feature extraction methods. For example, in remote sensing, the spectral characteristics of different land covers can vary significantly based on factors such as soil moisture, vegetation density, and topography. Deep learning algorithms can learn to capture these subtle differences in spectral characteristics and use them to classify the land covers accurately.
The most common deep learning architecture used in remote sensing is the Convolutional Neural Network (CNN). CNNs are designed to process images and learn spatial features from them. They consist of multiple convolutional layers, followed by pooling layers and fully connected layers. The convolutional layers apply a set of learnable filters to the input image, extracting different features at different scales. The pooling layers reduce the spatial dimensions of the feature maps, while the fully connected layers combine the features and produce the final classification output.
To train a deep learning classifier, a large dataset of labeled images is required. The dataset is typically divided into training, validation, and testing sets. The training set is used to update the network parameters iteratively through a process called backpropagation. The validation set is used to tune the hyperparameters of the model, such as learning rate and regularization strength, to achieve optimal performance. The testing set is used to evaluate the final performance of the trained model.
One of the advantages of deep learning classification is its ability to transfer knowledge learned from one dataset to another. Transfer learning is a technique in which a pre-trained network on a large dataset is fine-tuned on a smaller dataset for a specific task. This approach has shown promising results in remote sensing, where the availability of labeled data is limited.
