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The Digital Imaging and Remote Sensing (DIRS) Laboratory was formed over 30 years ago at the Rochester Institute of Technology (RIT) to serve as a focal point for remote sensing research and education at RIT. The DIRS Laboratory is one of the research laboratories within RIT's Chester F. Carlson Center for Imaging Science, an academic unit within the College of Science. DIRS focuses on the development of tools to extract information about the earth from aerial and satellite imaging systems with an emphasis on the application of science and engineering to solving end-to-end remote sensing problems using a systems engineering approach. This includes design and development of imaging instruments, developing algorithms to extract information from remotely sensed systems and measurement and modeling of the physical phenomena associated with the formation of remotely sensed images. DIRS currently has about 40 graduate students conducting advanced remote sensing research who are supported by 10 faculty and 10 full time research and administrative staff. DIRS has on-going research partnerships with multiple federal agencies, large and small companies, and other academic institutions.
News! Software developed by Aaron Gerace and Matt Montanaro, senior scientists in DIRS, improves the accuracy of NASA’s Landsat 8 Earth-sensing satellite, which was giving inaccurate readings due to defective optics in the thermal infrared sensor. Read more at RIT's University News: http://www.rit.edu/news/story.php?id=58199&source=enewsletter
To educate students and conduct research in the science, engineering and application of physics-based quantitative remote sensing and digital imaging systems (source-to-information) to aid in the solution of real-world problems.
Be known as the university laboratory of choice for education and research related to the physics-based phenomenology, modeling, collection, calibration, and analysis of remote sensing and digital imagery.
Our core values include integrity, dedication, respect, and commitment to excellence in our educational, research and professional activities.
Check out our Resources page for a listing of our equipment for image and ground reference data.
DIRS Group Meeting Schedule Spring 2017. We meet Wednesdays at 10 am in CAR-3215.
|1/25||Mashad Mahdavi||Roof Damage Assessment Using Deep Learning Techniques|
|2/1||Yihang Sun||Products to Aid ORNL's High-throughput Image Registration|
Don McKeown & Tim Bauch
|MX1 UAS Payload Overview|
|2/15||Chi Zhang||The Usage of Recurrent Neural Network in Video/Image Understanding|
Validation of Abundance Map Reference Data for Imaging Spectroscopy Unmixing
Efficient Generation of Image Chips for Training Deep Learning Networks
Estimating Top-of-atmosphere Thermal Infrared Radiance Using MERRA2 Atmospheric Data
A Pigment Analysis Tool for Hyperspectral Images of Cultural Heritage Artifacts
Contaminant Mass Estimation of Powder Contaminated Surfaces
FPN Pixel-Wise Linear Correction for Crime Scene Imaging CMOS Sensor
Improvements to an Earth Observing Statistical Performance Model
Characterizing the Temporal and Spatial Variability of Longwave Infrared Spectral Images of Targets and Backgrounds
Automatic Mission Planning Algorithms for Aerial Collection of Imaging-specific Tasks
Tyler Hayes & Nathan Cahill
Neural Network Applying for Reflectance Spectrum Classification
Piecewise Flat Embeddings for Hyperspectral Image Analysis
|4/12||No meeting due to SPIE DCS|
|4/26||Aneesh Rangnekar||Vehicle Tracking Using Hyperspectral Imagery: Challenges and Solutions|
Novel Chemical Detection Approach Using Sparse Methods
DIRS Group Meeting Schedule Fall 2016. We meet Wednesdays at 11 am in CAR-3215.
|9/7||John Kerekes||Overview of DIRS Laboratory and Projects|
|9/14||Zhaoyu Cui||Future Landsat Spatial-spectral Trade-off Study|
Jared van Cor
Machine Learning and Statistical Analysis for Reflectance Spectrum Classification
Quantifying the Relevence of Earthshine in Exoatmospheric Imaging
|9/28||Adam Goodenough||DIRSIG 5.0|
Validating DIRSIG 5 Via RAMI Scenes
Generating Labeled Data for Deep Semantic Segmentation in Remote Sensing Imagery
Predicting Above- and Below-ground Carbon Storage in Wetland Ecosystems
The Effect of Roughness on the Reflectance of Sediments
Improvements to an Earth Observing Statistical Performance Model with Applications to LWIR Spectral Variability
Stray Light Calibration for Landsat 8 TIRS
Quantification of Cynobacteria Blooms Using UAS Imagery
Automated Flight Planning Tools for Imaging Purposes
Introduction to the Spectral and Polarimetric Imagery Collection Experiment (SPICE) Data
Estimating Top-of-atmosphere Thermal Infrared Radiance using MERRA2 Atmospheric Data
Validation of abundance map reference data: more than you ever wanted to know about the data you always assumed were true
Anomaly detection in hyperspectral imagery: statistical vs. graph-based methods
A Survey on Machine Learning Methods for Hyperspectral Data Analysis
Roadway Flooding Detection and Depth Estimation
The Characterization of a DIRSIG Simulation Environment to Support the Inter-calibration of Spaceborne Sensors
Calibration Studies for the SOLARIS Sensor
Maksims Zigunovs (special guest)
|Applications of Neural Networks in Plant Recognition|