Introduction to Remote Sensing: How Can Satellite Images be Interpreted and Used | Part 12
We've also learned to correct image errors in brightness and geometry, ensuring we have high-quality data ready for analysis. Now, we explore two main approaches for interpreting images from remote sensing platforms. The first approach, photointerpretation, involves skilled analysts visually examining images to extract meaningful information. This traditional method relies on understanding color, brightness, and object relationships within images. The second approach leverages sophisticated computer algorithms for quantitative analysis, classification, and even machine learning. These methods involve human expertise in optimizing computer-based interpretations rather than direct image analysis.
We'll demonstrate how different band combinations create varied color images using data from the high map hyperspectral sensor. For instance, a natural color image displays vegetation in green and urban areas in brownish or bluish hues. A color infrared image, displaying near-infrared as red, visible red as green, and visible green as blue, highlights healthy vegetation in red and bare regions in blue and green. An infrared band combination image further differentiates cover types.
Join us as we delve deeper into the fascinating world of remote sensing image interpretation. Don't forget to like, share, and subscribe for more insightful lectures!
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