Detection of Sea Ice/ Melt Pond from Aerial Photos through Object-based Image Classification Scheme

Transcription

1 NEMC 213 August 5-9, San Antonio, TX Detection of Sea Ice/ Melt Pond from Aerial Photos through Object-based Image Classification Scheme Xin Miao 12, Hongjie Xie 2, Zhijun Li 3, Ruibo Lei 4 1 Department of Geography, Geology and Planning, Missouri State University 2 Department of Geology, University of Texas at San Antonio 3 Dalian University of Technology 4 Polar Research Institute of China

2 Introduction 1 Sea Ice and Climate Change The lowest Arctic summer sea ice extents with a new record summer minimum (3.4 million km2) set on 13 September 212. The shrinking summer sea ice extent could be the first of the tipping points of the climate dominos. Consequences: higher water temperature due to the positive feedback of albedo (melt ponds), more powerful and frequent storms, rising sea levels, diminished habitats for polar animals, and more pollution due to fossil fuel exploitation and/ or increased traffic. (Yohe et al., 27; Marshall, 213; Parkinson and Comiso, 213)

3 Introduction 2 High Spatial Resolution Image Processing Arctic sea ice models: e.g. Los Alamos Sea Ice (CICE) model coupled with POP (Parallel Ocean Program) in the NCAR CESM. Require high spatial resolution image data in the Arctic area to calibrate and verify the model, e.g. USGS Global Fiducials Library (GFL), and Survey aerial photos such as CHINARE 21 et al. Manually delineating of sea ice/ melt pond is timeconsuming and labor-intensive, need a reliable and efficient image batch processing scheme. (Kim et al., 26; Hunke, 21; Hunke and Lipscomb, 21)

4 Study Area Introduction 3

5 Altitude Introduction 4 Geotagging And Photo Selection July 26, 21 Aug 13/16/17/19, 21 Photo Selection Criteria F>4.5 (Stray light) Altitude > 8 m, Lag distance >12 m Lag Speed >72 kph = 2 m/s (Altitude exaggerated 3 times) Aug 22, 21 Lag distance 9 degree

6 Objectives Objectives Use object-based remote sensing classification scheme to extract melt ponds efficiently from 1,727 aerial photos taken during the Chinese Arctic Exploration in 21. Classification scheme: water, ice/snow, submerged ice, shadow and melt ponds. Evaluate the classification accuracy. Estimate the sea ice physical properties.

7 Flow Chart Literature review, data explore/ collection Statement of the project Method 1 Preprocessing Stray light calibration Resample photos Geotagging Photo selection Object feature extraction Image segmentaton Mean and SD of BV of RGB channels Texture: Variance/ Entropy Band ratio: Normalized Blue Index Shape: Area /Compact Photo interpretation Training Random Forest ensemble classifiers Calibration Accuracy assessment Accept Polygon neighbor analysis Calibration Final accuracy assessment Accept Output map

8 Method 2 Red Stray Light Removal test_ _12 test_ _92 Resolution: 12.1 megapixels CCD Size: 1/1.7 Zoom lens: 7.4 mm mm - F/ Focal Length Equivalent to 35mm Camera: mm Max Shutter Speed: 1/25 sec

9 Method 3 Resize Photos 3 * 4 pixels 75 *1 pixels

10 Image Segmentation (1) Method 4

11 Image Segmentation (2) Method 5

12 Image Segmentation (3) Method 6

13 Method 7 Feature Extraction Image segmentaton Mean and SD of BV of RGB channels Texture: Variance/ Entropy Band ratio: Normalized Blue Index Shape: Area /Compact Extract 13 Spectral, Texture, Band Ratio and Shape Features BR1= (B-R)/(B+R) BR2= (B-G)/(B+G) BR3=(G-R)/(2B-R-G) True Area/ Compact used in morphological analysis

14 Classification scheme Method 8 # Class Name Class Description 1 Water Arctic ocean, objects are rather dark and smooth. 2 Submerged ice Ice submerged under water along the edge, usually shown as color cyan or blue due to mixed reflection from ice surface and water. 3 Shadow Darker objects on the ice/snow caused by ridges and low solar elevation angle. Shadow will be combined into Ice/snow for calculation of ice concentration. Shadow will also be used for calculation of ridge height. 4 Ice/snow Bright white objects due to high reflectance of ice/snow. 5 Melt pond Pools of open water formed on sea ice. Melt pond will be used for calculation of fresh water volume.

15 Method 9 Random Forest: A Ensemble Method Feature extraction Classification Voting Output Image segmentaton DT1 Output 1 Mean and SD of BV of RGB channels 6 Texture: Variance/ Entropy Band ratio: Normalized Blue Index Shape: Area /Compact DT2 5 decision trees Output 2... m:: DTn Output n......

16 Polygon Neighbors Analysis (PNA) Method 1 Difficult to separate melt ponds from dark ice ; Melt pond should not be adjacent to water or submerged ice; Vector operation, good match for image segmentation; Still time consuming (14 h).

17 Method 11 Aerial photo processing & classification

18 Shadow Features Method 12

19 Major steps of shadow analysis Method 13

20 Blue BR3 Results 1 Cluster Analysis (Step 2 ) 25 2 Water Sub I ce Shadow Ice Water Sub I ce Shadow Ice Red 2 4 Green BR1.2.1 BR2

21 Out-of-Bag classification error Output Class Results 2 Classification (Step 2 ) Accuracy Sea Ice Classification Confusion Matrix Confusion Matrix % 1% % 1% % % 94.4% 5.6% % % 97.7% 2.3% Number of grown trees 1% 95.3% 4.7% 1% 1% Target Class 98.3% 1.7%

22 Feature Importance Results 3

23 Classification Result 1 Results 4

24 Classification Result 2 Results 5

25 Classification Result 3 Results 6

26 Classification Result 4 Results 7

27 Derivable Sea Ice Physical Properties Results 9 # Name Description 1 Ice concentration Fraction of ice-cover in a given area 2 Ice edge Boundary between an area of ice and open ocean 3 Floe size distribution Probability Density Function of ice floe diameters (or areas) 4 Melt pond distribution Probability Density Function of melt pool diameters(or areas) 5 Fresh water of melt Product of the areas and depths of melt ponds ponds 5 Lateral melting Melt rate of ice at the edges of ice floes 6 Surface roughness Probability Density Function of the elevation of ice above level ice 7 Ridge height Height of the (usually) linear features above the surrounding level or undeformed ice 8 Fractional heat transferring into the ocean Cumulative fraction of solar heat incident on ice/snow, submerged ice, melt ponds, and open water, weighted by the area and transmittance of each component

28 Conclusion 1 Conclusions Object-based classification is an efficient and accurate strategy to classify sea ice. Random forest can largely increase classification accuracy. Polygon neighbor analysis can extract spatial relationship between objects, providing further information to separate submerged ice and melt ponds. Sea ice roughness can be derived automatically from shadow. Errors often happen when a mixture of shadow, submerged ice and melt ponds.