Image Classification Techniques in Remote Sensing

Remote Sensing Image Classification Techniques
Remote Sensing Image Classification Techniques

What is Image Classification in Remote Sensing?

Image classification is the process of assigning land cover classes to pixels. For example, these 9 global land cover data sets classify images into forest, urban, agriculture and other classes.  

In general, these are three main image classification techniques in remote sensing:

  • Unsupervised image classification
  • Supervised image classification
  • Object-based image analysis

Unsupervised and supervised image classification techniques are the two most common approaches. However, object-based classification has been used more lately because it’s useful for high-resolution data.

What are some of the differences between supervised and unsupervised classification? Find out more by reading.

READ MORE: 15 Free Satellite Imagery Data Sources

Unsupervised Classification

Unsupervised Classification Example
Unsupervised Classification Example

In unsupervised classification, it first groups pixels into “clusters” based on their properties. In order to create “clusters”, analysts use image clustering algorithms such as K-means and ISODATA. For the most part, they can use this list of free remote sensing software to create land cover maps.

After picking a clustering algorithm, you identify the number of groups you want to generate. For example, you can create 8, 20 or 42 clusters. To be clear, these are unclassified clusters because in the next step, you manually identify each cluster with land cover classes. For example, if you want to classify vegetation and non-vegetation, you’ll have to merge clusters into only 2 clusters.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Unsupervised Classification Steps:

  • Generate clusters
  • Assign classes
Unsupervised Classification Diagram
Unsupervised Classification Diagram

Supervised Classification

Supervised Classification Example: IKONOS
Supervised Classification Example: IKONOS

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

Supervised classification uses the spectral signature defined in the training set. For example, it determines each class on what it resembles most in the training set. The common supervised classification algorithms are maximum likelihood and minimum-distance classification.

READ MORE: Supervised and Unsupervised Classification in ArcGIS

Supervised Classification Steps:

  • Select training areas
  • Generate signature file
  • Classify
Supervised Classification Diagram
Supervised Classification Diagram

Object-Based (or Object-Oriented) Image Analysis Classification

Object-based Classification
Object-based Classification

Supervised and unsupervised classification is pixel-based. In other words, it creates square pixels and each pixel has a class. But object-based image classification groups pixels into representative shapes and sizes. This process is multi-resolution segmentation or segment mean shift.

Multiresolution segmentation produces homogenous image objects by grouping pixels. It generates objects with different scales in an image simultaneously. These objects are more meaningful because they represent features in the image.

OBIA segmentation
Object-Based Image Analysis (OBIA) segmentation is a process that groups similar pixels into objects

But most importantly, you can classify objects based on texture, context and geometry.

OBIA classification
OBIA classification uses shape, size and spectral properties of objects to classify each object

In OBIA, you can use multiple bands to create objects and then classify them. For example, OBIA can take infrared, elevation or a shapefile to classify each object. Also, layers can have context with each other. For example, objects have proximity and distance relationships between neighbors.

Nearest neighbor (NN) classification is similar to supervised classification. After multi-resolution segmentation, the user identifies sample sites for each land cover class. Next, they define statistics to classify image objects. Finally, nearest neighbor classifies objects based on their resemblance to the training sites and the statistics defined.

Object-Based Classification Diagram
Object-Based Classification Diagram

Object-Based Nearest Neighbor Classification Steps:

  • Perform multiresolution segmentation
  • Select training areas
  • Define statistics
  • Classify

READ MORE: Nearest Neighbor Classification Guide in ECognition

Remote Sensing Data Trends

In 1972, Landsat-1 was the first satellite to collect Earth reflectance at 60-meter resolution. At this time, unsupervised and supervised classification were the two image classification techniques available. For this spatial resolution, this was sufficient.

However, OBIA has grown significantly as a digital image processing technique.

Image Classification Timeline
Image Classification Timeline

Over the years, there has been a growing demand for remotely sensed data. There are hundreds of remote sensing applications include food security, environmental concerns and public safety. To meet demand, satellite imagery is aiming at higher spatial resolution at a wider range of frequencies.

Remote Sensing Data Trends:

  • More ubiquitous
  • Higher spatial resolution
  • Wider range of frequencies

But higher resolution images does not guarantee better land cover. The image classification techniques used are a very important factor for better accuracy.

Remote Sensing Trends

Selection of Image Classification Techniques

Let’s say you want to classify water in a high spatial resolution image.

You decide to choose all pixels with low NDVI in that image. But this could also misclassify other pixels in the image that aren’t water. For this reason, pixel-based classification like unsupervised and supervised classification gives a salt and pepper look.

Humans naturally aggregate spatial information into groups. Multiresolution segmentation does this task by grouping homogenous pixels into objects. Water features are easily recognizable after multiresolution segmentation. This is how humans visualize spatial features.

  • When should you use pixel-based (unsupervised and supervised classification)?
  • When should you use object-based classification?
Spatial Resolution Comparison
Spatial Resolution: Low | Medium | High

As illustrated in this article, spatial resolution is an important factor when selecting image classification techniques.

When you have low spatial resolution, both traditional pixel-based and object-based image classification techniques perform well.

But when you have high spatial resolution, OBIA is superior to traditional pixel based classification.

Unsupervised vs Supervised vs Object-Based Classification

Image Classification Techniques Accuracy Assessment
Image Classification Techniques Accuracy Assessment

A case study from the University of Arkansas compared object-based vs pixel-based classification. The goal was to compare high and medium spatial resolution imagery.

Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. Because OBIA used both spectral and contextual information, it had higher accuracy. This study is a good example of some of the limitations of pixel-based image classification techniques.

READ MORE: 9 Free Global Land Cover / Land Use Data Sets

Growth of Object-Based Classification

Pixels are the smallest unit represented in an image. Image classification uses the reflectance statistics for individual pixels.

There has been much growth in the advancements in technology and the availability of high spatial resolution imagery. But image classification techniques should be taken into consideration as well. The spotlight is shining on the object-based image analysis to deliver quality products.

According to Google Scholar’s search results, all image classification techniques have shown steady growth in the number of publications. Recently, object based classification has shown much growth.

This graph displays Google Scholar’s yearly search results using the “AllinTitle:” search phrase.

The Growth of Image Classification Techniques for Publications
The Growth of Image Classification Techniques for Publications

If you enjoyed this guide to image classification techniques, I recommend that you download the remote sensing image classification infographic.

Image Classification in Remote Sensing


1. Blaschke T, 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65 (2010) 2–16
2. Object-Based Classification vs Pixel-Based Classification: Comparitive Importance of Multi-Resolution Imagery (Robert C. Weih, Jr. and Norman D. Riggan, Jr.)
3. Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation (Martin Baatz & Arno Schape)
4. Trimble eCognition Developer:


  1. With the aid of a diagram, describe the typical reflectance curve of a healthy vegetation in the visible and near-infrared portions of the electromagnetic spectrum.

  2. Thanks for this article. It is quite helpful. I have one question:
    Don’t you think there only two classification ways? Supervised and non-supervised (and ok maybe semi-supervised…) And then both of them can work pixel-based or segment-based? And inside the segment-case we could find at the highest level the object-based? I say this because I have been working for example in a non supervised approach object-based. And this doesn’t fix much in the classification as it is presented in this article. Thanks. Pablo

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