Modern Remote Sensing: Imagery, Capabilities, Possibilities Paul F. Hopkins Workshop on Advanced Technologies in Real-Time Monitoring and Modeling for Drinking Water Safety and Security

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Presentation Content Basic principles of remote sensing Traditional and modern imagery resources Digital image processing  Preprocessing  Information extraction  Accuracy assessment Additional topics in image processing  Modern approaches to information extraction  Change detection  Data fusion A few examples

Paul F. Hopkins Remote SensingWater Safety and Security Workshop What is Remote Sensing? Remote distant or without physical contact Perceiving or studying interesting properties and objects without physically contacting them Sense perceive, feel, or study properties or objects

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Remote Sensing Process Three general stages  Acquiring image data  Processing image data to produce information  Communicating and using information Expectations must be reasonable  Information providers and users need to be knowledgeable  We understand the capabilities of the “traditional” image data sources and applications  Much less understanding about recent remote sensing technologies

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Energy-Surface Interactions When energy strikes a surface, three interactions can occur:  Reflection  Absorption  Transmission Generally, in remote sensing, reflection is of most interest  Reflectance  Degree of reflectance varies with wavelength  For visible energy, spectral reflectance produces the colors we perceive

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Presentation Content Basic principles of remote sensing Traditional and modern imagery resources Digital image processing  Preprocessing  Information extraction  Accuracy assessment Additional topics in image processing  Modern approaches to information extraction  Change detection  Data fusion A few examples

Paul F. Hopkins Remote SensingWater Safety and Security Workshop “Traditional” Remote Sensing Aerial photography and expert processing  image interpretation (photointerpretation)  Image measurement (photogrammetry) Satellite digital imagery of moderate resolution and computer processing  Weather satellites, Landsat, SPOT, and IRS  Image processing procedures –Enhancements and transformations –Spectral pattern recognition  Data continuity

Paul F. Hopkins Remote SensingWater Safety and Security Workshop “Modern” Remote Sensing (data) High spatial resolution digital imagery  On the order of 1m or better resolution  Exceptional spatial detail but many new challenges High spectral resolution imagery  Dozens, if not hundreds, of spectral bands  Fundamental changes in processing data High temporal resolution imagery Radar (microwave) digital imagery  Operational advantages  Information content very different than others Lidar

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Idea of Spatial Resolution 1 meter pixel 30 meter pixel

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Comparison of Spectral Resolutions MULTISPECTRAL Number of Bands: Tens Bandwidth : Wide (micrometers (  m)) (micrometers (  m)) Spectral Resolution: Medium HYPERSPECTRAL Number of Bands: Hundreds Bandwidth: Narrow (nanometers (nm)) (nanometers (nm)) (Narrower in reflective region than in emissive region) Spectral Resolution: High ULTRASPECTRAL Number of Bands: Thousands Bandwidth: Very Narrow (<1 nanometer) (<1 nanometer) Spectral Resolution: Very High Detects solids and liquids Detects and identifies solids, liquids, and some gases Detects and identifies solids, liquids, and gases

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Imaging Spectroscopy A single pixel A Pixel Hyperspectral data cube North East Spectral Hyperspectral data provides considerable information about the surface materials Multispectral imagery provides only a few channels of information Multispectral Reflectance

Paul F. Hopkins Remote SensingWater Safety and Security Workshop HYPERION Satellite system with spatial resolution: 30 m Spectral resolution: 220 bands (from 0.4 to 2.5 µm)

MODIS Spectral resolution: 36 discrete spectral bands  Bands 1-19 in the range of 620 to 965 nanometers  Bands in the range of 3.6 to 14.3 micrometers Spatial Resolution: Varies from 250 m to 1000 m Temporal: Entire Earth every one to two days Suited for regional applications Snow Cover to north Clouds to east February 28, 2002

Paul F. Hopkins Remote SensingWater Safety and Security Workshop ASTER Spectral resolution: 14 discrete spectral bands Spatial: Varies band to band  15 m (bands 1-3 VNIR)  30 m (bands 4-9 SWIR)  90 m (bands Thermal) VNIR band 3 has both forward and nadir looking components to produce stereo imagery Onondaga Lake Syracuse, NY June 19, 2000

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Backscatter Coefficient Images JERS-1 (April, 95) ERS-1 (August, 95)

Direct Geopositioning

Paul F. Hopkins Remote SensingWater Safety and Security Workshop LIDAR LIght Detection And Ranging The LIDAR instrument transmits light out to a target Some of this light is reflected and/or scattered back to the instrument where it is analyzed The change in the properties of the light enables some properties of the target to be determined The time for the light to travel out to the target and back is used to determine the range to the target Direct Geopositioning is crucial Digital Elevation Model or DEM is often produced

Paul F. Hopkins Remote SensingWater Safety and Security Workshop LIDAR image Tully Valley NY

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Presentation Content Basic principles of remote sensing Traditional and modern imagery resources Digital image processing  Preprocessing (restoration and enhancement)  Information extraction (classification)  Evaluation (accuracy assessment) Additional topics in image processing  Modern approaches to information extraction  Change detection  Data fusion A few examples

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Radiometric Restoration (sensor problems)

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Radiometric Restoration Downwelling Absorption & Scattering Direct & Adjacent Reflection Upwelling Absorption & Scattering Atmospheric Path Radiance

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Geometric Restoration

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Contrast Enhancement

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Ratios and Indices

Spectral Transform (PCA) 1 2 3

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Classification Perform statistical pattern recognition  Assume spectral (or other) measurements have unique patterns for the classes of interest  Use computer routines to generate statistical descriptions of these patterns and classes  Relate image values to the statistical descriptions of classes –Identify a strategy for deciding which class is most similar to the image location under consideration –Apply the decision strategy to all image values and assign class identities Postprocess, if desired

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Idea of Training

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Classification Result

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Accuracy Assessment Uses idea of a contingency or confusion table (also termed an “error matrix”) Compare a sample of reference locations with the class assigned by the classifier Classified Category Reference CategoryTotal AB A90696 B Total

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Presentation Content Basic principles of remote sensing Traditional and modern imagery resources Digital image processing  Preprocessing  Information extraction  Accuracy assessment Additional topics in image processing  Modern approaches to information extraction  Change detection  Data fusion A few examples

Paul F. Hopkins Remote SensingWater Safety and Security Workshop “Modern” Remote Sensing (processing) Complementary technologies (GPS, GIS) Analytical photogrammetry and methods for geometrically processing imagery (DOQQs) Information technology and computer processing, generally and specifically for image processing  Image/Spatial modeling & expert classifiers  Adaptive computing  Change detection  Data fusion

Example Image Model (to find trees in high resolution imagery)

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Input Image

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Model Result

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Example of Adaptive Computing (Genetic Algorithm Approach) First rowSecond row Template of Tree Crown Chromosome

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Example Templates Manually generated GA- evolved

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Genetic Algorithm Output Manually generated template GA evolved template

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Genetic Algorithm Results ClassifiedTreeNot Tree User’s Accuracy Tree % Not Tree % Producer’s Accuracy 100%60%Overall: 80% ClassifiedTreeNot Tree User’s Accuracy Tree % Not Tree 44090% Producer’s Accuracy 90%100%Overall: 95% Manually generated template GA evolved template

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Change Detection Numerous methods and the best method will depend on the type and amount of change Accurate registration is critical Some methods require accurate normalization to remove variations that are not caused by land changes  Atmosphere  Energy source – target – sensor variations Errors in the input images will compound each other and produce greater errors in the change detection results

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Image Fusion Many different image types (resolutions) Combining more than one type might provide enhanced capability  Sharpening with higher spatial resolution data  Phenological exploitation with high temporal resolution data  Enhanced spectral pattern distinctions with higher spectral resolution data Selected methods  Intensity – hue – saturation (ihs) transforms  Principal component substitution  High pass frequency substitution

Paul F. Hopkins Remote SensingWater Safety and Security Workshop IHS Transform

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Principal Component Substitution

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Presentation Content Basic principles of remote sensing Traditional and modern imagery resources Digital image processing  Preprocessing  Information extraction  Accuracy assessment Additional topics in image processing  Modern approaches to information extraction  Change detection  Data fusion A few examples

Paul F. Hopkins Remote SensingWater Safety and Security Workshop

Paul F. Hopkins Remote SensingWater Safety and Security Workshop

Paul F. Hopkins Remote SensingWater Safety and Security Workshop

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Onondaga Lake ASTER Image (19 June 2000)

Paul F. Hopkins Remote SensingWater Safety and Security Workshop Onondaga Lake Emerge Imagery (July 1999)

Paul F. Hopkins Remote SensingWater Safety and Security Workshop LIDAR Application

Paul F. Hopkins Remote SensingWater Safety and Security Workshop