Modelli tematici 3 D dell uso del suolo a partire da DTM e immagini telerilevate ad alta risoluzione WorldView 2

Transcription

1 University of Naples Parthenope Department of Applied Sciences Modelli tematici 3 D dell uso del suolo a partire da DTM e immagini telerilevate ad alta risoluzione WorldView 2 3 D thematic models of land use from DTM and high resolution satellite images WorldView 2 Pasquale Maglione, Claudio Parente, Raffaele Santamaria, Andrea Vallario GIT Geology and Information Technology 8 Convegno Internazionale del Gruppo di Geologia Informatica Chiavenna (So) Giugno 201 3GIT Geology and Information Technology

2 OUTLINE I Introduction II - Aim III High resolution images IV Data and methods V Results and discussion VI - Conclusions

3 Applications of Remote Sensing Meteorology (Weather Prediction) Climatology Oceanography Costal Studies Water Resources Geology Archeology Land cover\land use

4 Band Combinations 3,2,1 4,3,2

5 Land use Classification Defining the pieces that make up the puzzle

6 Land cover classification steps Define why you want a classified image, how will it be used? Decide if you really need a classified image Define the study area Select or develop a classification scheme (legend) Select imagery Prepare imagery for classification Collect ancillary data Choose classification method and classify Adjust classification and assess accuracy

7 Basic Strategy Different objects have different spectral signatures Vegetation Soil Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

8 Aim This presentation is aimed to demonstrate that high resolution satellite images such as WorldView 2 in combination with adequate DTMs (Digital Terrain Models) can be used to generate high quality 3 D thematic models.

9 Data WorldView-2 Satellite Launch 8 October 2009 Altitude Orbit type 770 km Sun synchronous, 10:30 am descending node Inclination 97,2 Period Swath Width 100 min 16,4 km Revisit Frequency 1.1 days (GSD 1 m) Revisit Frequency at nadir 14 days

10 Data Pancromatic WorldView-2 Geometric resolution: Band : 0.50 m x 0.50 m ( ) μm Radiometric resolution: 11 bit

11 Tipologia dei dati utilizzati Multispectral WorldView-2 Geometric resolution 2 m x 2 m Bands: Coastal Blu Verde Yellow Rosso Red Edge NIR1 NIR2 ( ) μm ( ) μm ( ) μm ( ) μm ( ) μm ( ) μm ( ) μm ( ) μm Radiometric resolution: 11 bit

12 1 Study area UL: LR: UTM/WGS84 Zone 33T Extention m E m N m E m N

13 Data Analysis 1)Image Rectification and Restoration Geometric Correction Radiometric Correction Noise Removal 2)Image Enhancement The objective is to create new images from the original image data in order to increase the amount of information that can be visually interpreted from the data. 3) Image Classification

14 1 Geometric Correction

15 Classification Strategies Two basic strategies Supervised classification We impose our perceptions on the spectral data Unsupervised classification Spectral data imposes constraints on our interpretation

16 Supervised Classification Steps 1. Decide on classes. 2. Choose training pixels which represent these classes. 3. Use the training data with a classifier algorithm to determine the spectral signature for each class. 4. Using the classifier, label each pixel in an as one of the pre-determined classes (or potentially an other class 5. Accuracy Assessment test/validation data for accuracy assessment

17 Supervised Classification Supervised classification requires the analyst to select training areas where he/she knows what is on the ground and then digitize a polygon within that area The computer then creates... Mean Spectral Signatures Conifer Known Conifer Area Known Water Area Water Known Deciduous Area Deciduous Digital Image

18 Supervised Classification Mean Spectral Signatures Multispectral Image Information (Classified Image) Conifer Deciduous Water Unknown Spectral Signature of Next Pixel to be Classified

19 Supervised Classification Common Classifiers: Parallelpiped Minimum distance to mean Maximum likelihood

20 Maximum likelihood Max likelihood uses the variance and covariance in class spectra to determine classification scheme. It assumes that the spectral responses for a given class are normally distributed.

21 Maximum Likelihood

22 Maximum Likelihood Classifier Mean Signature 1 Candidate Pixel Mean Signature 2 It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures Blue Green Red Near-IR Mid-IR

23 Maximum Likelihood Classifier Mean Signature 1 Candidate Pixel Mean Signature 2 The candidate pixel clearly belongs to the signature 2 group. Blue Green Red Near-IR Mid-IR

24 Training Sites - Considered Factors Number of Training/Calibration Areas depends on (i) # of classes; and (ii) diversity of classes many smaller areas better than a few large areas Number of Training/Calibration Pixels 10N to 100N pixels; where n = # of spectral bands (Lillesand and Kiefer) depends on the environment, but at least 100+ pixels per class accumulated from several training areas (Campbell) >10N pixels where n = no. of spectral bands (Jensen)

25 1 Classification Training Producer Superfici 0.99 asfaltate Infrastructure (Roads,...) User Coperture 1 1 in lamiera Metal structure Volcanic area (solfatara) Overall Zona vulcanica 0.97 solfatara Lake Cohen Acque lacustri 0.97 Vegetazione Mediterranean arborea vegetation Agricoltural areas Vegetazione non arborea Test Campi Sports sportivi fields Producer Terra Soils battuta User Edificato Urban areas (Res., ind.) Overall 0.84 Cohen 0.78

26 1 DTM CTR - 1:5000 DTM Geom. Res. : 2 m

27 1 Classification Natural areas (Mediterranean Vegetation) Agricolture areas

28 Conclusions WorldView2 multispectral images supply strong possibilities for accurate classification of land cover/use. Using multispectral data, positional original accuracy (2m x 2 m) is ensured if appropriate geometric corrections are apported with Rational Polynomial Functions based on GCPS from maps 1: Thematic accuracy is accettable (more than 85% ) with adeguate application of Maximum Likelihood Classification. For 3 d thematic models, positional and altimetric accuracy are garanted with DTM obtained from cartographic data 1:5.000.

29 Thanks for attention