Remote Sensing Introduction to the course

Remote Sensing Introduction to the course

Remote Sensing (Prof. L. Biagi) Exploitation of remotely assessed data for information retrieval Data: Digital images of the Earth, obtained by sensors recording electromagnetic radiation reflected or emitted from the earth surface, each one recording only a part of the radiation contained within a spectral band. Remotely assessed: Mostly from orbiting satellites, less from airplanes Retrieved information: Land coverage, Environmental /meteorological parameters, Surfaces, Cartography Exploitation: Thematic mapping, etc.

Remote Sensing: Multispectral satellite images: environmental analysis/ thematic mapping Hyperspectral airborne /satellite images: environmental /metereological analysis RGB / pancromatic high resolution satellite images: cartographic applications Remote sensed data for the reconstruction of Digital Surface Models Terrestrial LIDAR : surface analysis

Remote Sensing: The main vehicle of information remotely sensed is energy in form of Electro Magnetic Radiation (EMR), emitted, reflected or transmitted by a target surface. ΔΩ P ΔΑ

The Electromagnetic Spectrum A cm λ 0.1 1 10 10 2 10 3 10 4 10 5 10 6 0.1 1 10 10 2 10 3 10 4 10 5 10 6 10 7 A µ cm m km 0.3 3 30 300 0.3 3 30 300 0.3 3 30 3 30 300 3 30 300 γ RADAR RADIO AUDIO AC Χ MICROWAVES UV IR VISIBLE UV (Ultraviolet) ç Violet Red è IR (Infrared)

Details in the (near) visible bands

The Fundamental Concept of Remote Sensing A. The physical Reality (idealized) ρ 0.5 V S V S W 0.5 0.5 W λ (µm) 1.0 1.5 2.0 Observed land area consisting of 3 types of land coverage: W: Water S: Soil V: Vegetation Each land cover type reflects a certain fraction ρ of the solar electromagnetic radiation different for each wavelength constituent λ Τhe function ρ(λ) is the spectral signature of the particular land coverage

Spectral bands of the Thematic Mapper (T1, T2, T3, T4, T5) (Landsat) and HRVIR (S1, S2, S3, S4) (SPOT) 1. water 2. vegetation 3. bare soil 4. snow

Landsat Thematic Mapper 7 bands Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Band 6 False colors composition 3 2 1 4 3 2 7 4 2 7 5 4 5 A.7Dermanis, 4 L. Biagi

IKONOS high resolution image of Como

The Fundamental Concept of Remote Sensing A. The physical Reality (idealized) ρ 0.5 V S V S W 0.5 0.5 W λ (µm) 1.0 1.5 2.0 Observed land area consisting of 3 types of land coverage: W: Water S: Soil V: Vegetation Each land cover type reflects a certain fraction ρ of the solar electromagnetic radiation different for each wavelength constituent λ Τhe function ρ(λ) is the spectral signature of the particular land coverage

The Fundamental Concept of Remote Sensing B. The Data Capture Band 1 Band 2 Band 3 v 2 ρ V 0.5 s 3 s 2 v 3 S w 1 w 2 w 3 s1 v 1 0.5 0.5 W λ (µm) 1.0 1.5 2.0 v 1 s 1 w 1 v 2 s 2 w 2 v 3 s 3 w 3 Band 1 Band 2 Band 3

The Fundamental Concept of Remote Sensing C. The Data Analysis The 3 bands formulate a 3-dimensionl fictitious space the multispectral space x 3 S V The values of a pixel in the 3 bands are its coordinates in the multispectral space w 2 s 2 v 2 v s w 1 1 1 x 1 W s v 3 3 x 2 Pixels in the same class have similar band values and they are close in multispectral space Pixels in different classes have different band values and they are far away from each other in multispectral space

Pattern recognition: classification of objects based on their shapes 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 Assume: 1: grass, 2: concrete. In this case: 1: crops 2: road LIKELY 1: gardens 2: buildings UNLIKELY

Pattern recognition: classification of objects based on their shapes 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 1 1 2 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 1 2 2 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 1 2 2 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 Assume: 1: grass, 2: concrete. In this case: 1: crops 2: road UNLIKELY 1: gardens 2: buildings LIKELY

REMOTE SENSING AND RELATED SCIENCES SUPPORT DATA Geodesy Surveying Photogrammetry etc. Remote Sensing ñ Photointerpretation ñ Photogrammetry Thematic Cartography GIS USERS DEVELOPMENT

Interaction of Remote Sensing with other scientific fields Remote Sensing Digital Image Analysis Pattern Recognition

Applications: Land Use / Land cover

Applications...

DATA INTERPRETATION HETEROGENEOUS DATA FUSION Multispectral classification +pattern recognition results merging Thematic map and ground surveys joined analysis DERIVED THEMATIC MAPS Composite derived spatial indexes: aquifer Vulnerabily, hydrogeological risk, landslide risk ACCURACY MAPS A spatial assessment of the estimated derived spatial indexes

COURSE, CLASSWORK and EXAM COURSE 36 hours of lectures CLASSWORK Compulsory participation!) 12 hours of classwork: ENVI (? + MatLab?) EXAM Written reports during classworks + oral examination

CONTENTS of the COURSE FUNDAMENTALS Introduction Electromagnetic Radiation Satellites and Sensors DATA PREPROCESSING Geometric Correction and Orthorectification (Georeferencing) Atmospheric Influence and Radiometric Correction

CONTENTS of the COURSE DIGITAL IMAGE ANALYSIS Histogram Manipulation Template and Fourier Filters BAND TRANSFORMATIONS Band Algebra & Vegetation Indices Principal Components and Tasseled Cap

CONTENTS of the COURSE MULTISPECTRAL AND GEOMETRIC CLASSIFICATION The Classification Problem Clustering and Unsupervised Classification Statistical Supervised Classification Training and Decision functions Training and Neural Networks Texture analysis INTERPRETATION Heterogeneous data fusion Derived thematic maps Derived accuracy maps

DEMs Data sources: SAR and LiDAR Raw observations filtering DEM s and DTM s The principal global models DTM s applications