Purpose of the Analysis :
Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data from different sensors such as Earth observation satellites, manned aircrafts and UAV imagery are primary data sources extensively used for change detection over the last decade. This is an introductory, simplified imagery analysis to demonstrate spatial, spectral, radiometric and temporal changes in Earth Observation capabilities.
In this post, we will keep the context within the spatial and spectral resolution of aerial imagery.
This is a simplified and limited scope post. We are able to provide further stats, explanation and custom solutions. Please contact us to discuss your business case further.
Earth Observation Imagery Resolution and Quality Trade-Off
There are various change detection techniques which have been developed over the past decade. Pixel-based change detection has been, and remains, an important research topic in remote sensing. When observing a common scene, a high-spatial-resolution image provides more details than a lower-spatial-resolution image. However, this increased spatial resolution generates high spectral variability within geographic objects, which typically reduces the change detection and classification accuracy when using pixel-based algorithms.
Image classification is another important process for extracting information classes, such as land cover categories or human-made objects, from multi-band remote sensing imagery or aerial imagery, and still one of the most challenging problems in understanding high-resolution remote sensing images Deep learning techniques, especially the convolutional neural network (CNN), have improved the performance of remote sensing image scene classification due to the powerful perspective of feature learning and reasoning, but traditional methods such as pixel-based and object-based classification are appropriate for most of the cases and still used in many studies.
In the above imagery, the revisit time is 2 years. Please review the circled, human made buildings and vegetation change.
Parameters to decide the suitable data type for your business depend on your ultimate goal or mission. While there are so many aerospace and computer science technologies, we observe increased misinformation in the market, leading businesses to miss the value of data driven decision making. Some of the application areas include but not limited to object detection, construction progress analysis, earth surface changes, forestation, inspections, insurance claims review…etc. Please feel free to reach out to us to discuss your business case.
Aerial Image Resolution Characteristics
Key Advantages of Aerial Platforms and Data
Satellite Imagery Pros : Global data coverage. Frequent revisit time as low as 30 minutes to few hours. Low cost raw imagery. Access to historic imagery. Wide spectrum of sensors (EO, IR, SAR, Multi-Spectral…)
Satellite Imagery Cons : Lower resolution (30cm to 30 meters) compared to low altitude aircraft imagery.
Manned Aircraft Imagery Pros: Ability to carry very high resolution and heavy sensors. Ability to fly over cities with easier restrictions.
Manned Aircraft Imagery Cons: Expensive compared to satellites. Low availability. Requires crew or professionals
UAV Imagery Pros: Ability to fly low altitude. Carry sufficient payload for missions up to 1-2 hours. Commercially available easily. Low CapEx and OpEx
UAV Imagery Cons: Flight regulations and restrictions on populated areas and cities. Limited options and flight time for heavy sensors such as Lidar.