Computer Vision in Satellite Imagery for Geospatial Analysis
Satellite imagery provides a reliable stream of data for monitoring the Earth's surface at regional, national, and global scales; however, large satellite imagery datasets require considerable time and effort. Nevertheless, extracting relevant information from large datasets of satellite imagery requires considerable time efforts. This problem is solved by means of applying computer vision techniques to satellite imagery. They allow automatic recognition, classification, segmentation, and analysis of geographic objects through the use of image processing, machine learning, and deep learning algorithms.
Using computer vision with the help of GIS and remote sensing technologies helps companies turn raw satellite images into organized geospatial data.
What Is Computer Vision for Satellite Imagery?
Computer vision is an area within artificial intelligence that allows machines to interpret and extract information from visual data. In satellite imagery, computer vision approaches are tailored to deal with geospatial raster data, such as multispectral, hyperspectral, thermal, and SAR imagery.
Unlike standard photographs, satellite images are geospatial and spectral in nature and therefore computable. A single pixel may carry spectral information in several bands, which allows the machine to distinguish between various land cover types such as vegetation, water bodies, soil, roads, and buildings.
A common computer vision workflow for satellite imagery is as follows:
Acquisition of satellite imagery
Preprocessing, radiometric or geometric
Correction of atmospheric or sensor effects
Image enhancements and features extraction
Training of the model or inference
Object detection or image segmentation
Postprocessing, geospatial
GIS analysis and visualization
How Computer Vision Works With Satellite Imagery
The computer vision algorithms detect patterns in pixels or groups of pixels. Based on the application, the input may be in the form of one band image, RGB images, multispectral bands, spectral indices, or raster layers.
For example, vegetation and urban land cover can be separated based on the spectral properties of near-infrared and visible bands. The model would learn patterns in space and spectra related to vegetation and classify the pixels within a very large satellite image.
Modern deep learning algorithms are capable of learning the spatial context as well. In other words, CNNs, vision transformers, and segmentation algorithms are capable of detecting the shapes, textures, and relationships of neighboring pixels.
Key Computer Vision Tasks in Satellite Image Analysis
Image Classification
In image classification, a class is assigned to an entire image or an image tile. Some classes that can be determined by classification in remote sensing include:
Forest
Cropland
Water
Urban area
Grassland
Bare soil
Wetlands
Classification algorithms could use spectral information, texture characteristics, and spatial information to classify different surface types.
Semantic Segmentation
Semantic segmentation involves assigning classes to each pixel in an image. It is helpful in developing land-cover maps.
For example, a satellite image could be segmented into water, vegetation, road, building, and bare soil categories. Such a raster map can then be incorporated into a GIS for further spatial analysis.
Some of the popular deep learning architectures for semantic segmentation include U-Net and DeepLab architectures.
Object Detection
Object detection involves recognizing specific objects and generating bounding boxes around them. Using satellite images, machine learning models can detect objects such as:
Buildings
Vehicles
Ships
Aircraft
Solar panels
Storage tanks
Wind turbines
Object detection is especially helpful when there is a requirement to identify and count specific geographic objects.
Instance Segmentation
As opposed to semantic segmentation, instance segmentation helps recognize specific objects separately from one another.
For example, a semantic segmentation algorithm will label some part of an image as “building,” whereas an instance segmentation algorithm will identify all the buildings as separate objects.
Change Detection
Change detection involves comparing images from different time periods to discover changes on the surface of the Earth.
Computer vision can be applied to detect:
Urbanization
Deforestation
Flood
Construction
Damage due to fire
Agricultural changes
Changes along the coast
Infrastructure construction
The simple process for change detection would involve comparing two registered satellite images and computing the differences between them.
Satellite Data Used in Computer Vision
Various satellite sensors offer various kinds of data that can be used in computer vision.
Optical Satellite Images
Optical satellites collect the electromagnetic energy that is reflected at visible and infrared wavelengths. Optical images are commonly used for the classification of cover type, vegetation mapping, urban areas, and environmental studies.
Examples of optical satellite images are Sentinel-2 and Landsat datasets.
Multispectral Imagery
Multispectral images contain several spectral bands. Extra spectral bands enable learning algorithms to classify various materials that look alike in the RGB image.
For example, vegetation shows high reflectivity in the near-infrared range of wavelengths. Multispectral images have many uses for vegetation mapping.
Hyperspectral Images
Hyperspectral sensors collect many narrow spectral bands. This gives much information on materials’ spectra that can be applied in the identification of minerals, vegetation, and other applications.
Synthetic Aperture Radar
As SAR imagery utilizes microwave radiation instead of visible light, it can support many applications because it is independent of weather conditions such as cloudy atmospheres or daytime/nighttime.
Computer vision techniques can be implemented to work on SAR imagery for flood mapping, ship detection, infrastructure monitoring, and land-surface change analysis.
Deep Learning for Satellite Imagery
The introduction of deep learning to computer vision has revolutionized satellite imagery analysis. Neural network models are able to autonomously extract complex features from large satellite imagery datasets labeled and unlabeled.
There are several types of neural networks, including convolutional neural networks, which are widely used in satellite imagery analysis for image classification, object detection, and segmentation. Recently, transformer architectures have been gaining popularity for geospatial information extraction.
A common deep learning pipeline is as follows:
Satellite imagery → Preprocessing → Training data → Model training → Inference → Post-processing → GIS analysis
The quality of the training data plays an essential role in the efficiency of machine learning. Unlabeled or poor labeling, inconsistencies in classes, geographic bias, etc., all negatively influence the results of the model.
Preprocessing Satellite Imagery for Computer Vision
Satellite images normally require preprocessing before being fed to the computer vision model.
Some important preprocessing techniques can be:
Atmospheric Correction
The presence of atmospheric conditions can affect the spectral values obtained through an optical sensor. Atmospheric correction aims to achieve surface reflectance values that are more appropriate for analysis.
Geometric Correction
Geometric corrections make sure that the pixels are aligned properly with geographical coordinates.
Cloud and Cloud Shadow Masking
Clouds and cloud shadows can affect the computer vision model. The use of a cloud mask can eliminate contaminated pixels from the analysis.
Resampling
The spatial resolutions of different satellite bands might vary. Resampling helps in aligning bands onto the same pixel grid before merging them together.
Image Tiling
Large satellite images are usually tiled into smaller images to train and make inference with models. This step also helps in making large data sets easier to process using GPU-powered methods.
Normalization
Normalization of pixel values helps to enhance model training and performance.
GIS Integration
Computer vision technology becomes even more beneficial when its results are integrated within the GIS context.
Model results may comprise classified raster data, probability maps, bounding boxes, points, or polygon vectors. All this information may be merged with other geographical layers in order to conduct various kinds of analyses.
For example, detected building footprints may be clipped against the administrative boundaries in order to find the number of buildings per district. Road segments identified through the computer vision model may be compared against already existing transportation datasets to detect any modifications.
The standard procedure may be the following:
Satellite images → Computer vision model → Feature extraction → Vectorization → GIS analysis
Output data may be saved in various geospatial formats like GeoTIFF, Cloud Optimized GeoTIFF (COG), GeoJSON, GeoPackage, Shapefile, and others.
Technical Issues in Satellite Imagery Computer Vision
While significant progress has been made, there are still some technical issues in satellite image analysis.
Spatial Resolution
Smaller objects may be represented by a handful of pixels even in medium-resolution imagery. Increasing resolution may improve object detection, but higher-resolution images are harder to store and analyze.
Cloud Cover
Clouds and their shadows may obscure surface objects from optical imagery. Multi-temporal imagery and cloud masking can mitigate this issue.
Geographical Generalization
A trained model for one geographical area is unlikely to work just as well for another since environments and cultural specifics may be vastly different.
Costly Training Dataset
Generating a good training dataset may take a lot of time and money. Models may also work badly with class imbalance in datasets.
Large Image Datasets
Satellite imagery programs constantly produce vast amounts of data that have to be stored and processed efficiently.
Model Interpretability
The black-box nature of deep learning models may make them extremely hard to interpret.
The Future of Computer Vision in Satellite Imagery
The fusion of Earth observation and artificial intelligence is gradually progressing towards an increasingly automated geospatial analysis process. Thanks to foundation models, self-supervised learning, multimodal AI, and transformers, models have become capable of learning from large and varied datasets of Earth observation.
Rather than developing a new model for each task, geospatial foundation models allow for the development of representations that can be customized for classification, segmentation, object detection, and change detection applications.
Geo-cloud infrastructure is another aspect that affects satellite image processing. Cloud native formats, like Cloud Optimized GeoTIFF, and scalable computing systems enable access to large raster datasets without downloading the whole scene to a local environment.
Such trends contribute to the creation of a GeoAI ecosystem in which computer vision, remote sensing, GIS, and machine learning cooperate to transform satellite observations into valuable geographic data.
Computer vision of satellite imagery in geospatial analysis involves the use of image processing, machine learning, deep learning, remote sensing, and GIS to extract useful information from observations of the Earth. Methods like classification, semantic segmentation, object detection, instance segmentation, and change detection allow for automated recognition and monitoring of geographical features on a large scale.
With the increase in the size of satellite databases, computer vision processes will play a vital role in transforming vast quantities of raster imagery into geospatial information. Deep learning, spectral analysis, cloud-native data types, and GIS form the basis of scalable satellite image analysis.
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