In this lab for REM-617-01: Image Analysis & Information Extraction, we performed image manipulation and atmospheric correction using a Landsat ETM+ scene in ENVI. The assignment required stacking spectral bands, interpreting RGB composites, creating a Region of Interest (ROI), building a mask, computing statistics, and applying Dark Object Subtraction (DOS). The objective was to move from raw satellite data to
Tag: remote sensing
Intro This project demonstrates how an existing deep learning model can be refined to perform better on local data. It walks through how transfer learning allows a pretrained model to adapt to new imagery and conditions, using ArcGIS Pro as a complete workspace for deep learning. Preparing the Project The lab begins with the Seattle_Building_Detection project, which contains NAIP aerial
Every pixel carries a truth invisible to the eye. Hyperspectral imaging doesn’t just capture color, it captures the chemistry of the world. Intro This post is based on one of our lab assignments in Applied Remote Sensing at Delta State University. The task was to explore the power of hyperspectral imagery, the kind of data that captures hundreds of spectral
Introduction: From Pixels to the Real World Every satellite image tells a story, but that story only makes sense when you can trace each pixel back to the ground. In remote sensing, this connection between imagery and reality is called ground truthing. It ensures that what we interpret from orbit reflects what truly exists on Earth. As Campbell and Wynne
There is a quiet precision in Lidar work. Every return pulse is a story, every elevation layer a whisper of the terrain. What looks like color and code is a dialogue between light and surface. This lab took that conversation apart, from raw LAS files to a three-dimensional rendering of life above and below the canopy. Importing Lidar Data and