Joint Detection and Localization of Cumulonimbus (Cb) Clouds in Satellite Images

Transcription

1 Andrew and Erna Viterbi Faculty of Electrical Engineering Signal and Image Processing Lab. Joint Detection and Localization of Cumulonimbus (Cb) Clouds in Satellite Images Students: Etai Wagner, Ron Dorfman Supervisor: Almog Lahav Semester: Spring, 2017 Date: [02/07/2018] In Collaboration with:

2 Agenda Project Goal Background and Motivation Existing Solutions Proposed Solution Results Conclusions and Future Work 2

3 Data Description 6 sequences of 8 IR images with time resolution of 15 min. frame Each image is Images labeled by an expert meteorologist 3

4 Project Goal Identification and Localization of Cumulonimbus (Cb) clouds in Infrared satellite images (IR) Develop spatio-temporal analysis framework that takes into account the unique characteristics of Cb clouds 4

5 Background and Motivation Cb clouds associated with thunderstorms and atmospheric instability Top of the cloud takes an anvil shape as the tropopause blocks its vertical development High typical rain causes limitations to RF devices Dangerous to air traffic 5

6 Existing Solutions An automated method to track Cb clouds based on cloud classification using Neural Network (NN) and Seeded Region Growing (Liu et al., 2014) A classifier for storm detection based on extracted features, e.g., divergence of cloud s motion field (Zhang et al., 2017) 6

7 Cb Temporal Characterization Cb clouds have radial expansion over time Goal: quantify this radial expansion Solution: calculate flux through segments 7

8 Temporal Anomaly Detection 2 input frames Construct motion field F Segmentation Divergence Flux through segments Flux image, Φ flux F, S = div F i, j i,j S 8

9 Temporal Anomaly Detection - Example 9

10 Cb Spatial Characterization Typically colder Usually have defined borders Not Cb Cb 10

11 Spatial Anomaly Detection Unsupervised spatial anomaly detection using diffusion maps (Mishne and Cohen, 2013) Dimensionality reduction to the patches of an image An anomaly score A 0,1 for each patch is based on the local density in the low-dimensional space Low density neighborhood high anomaly score A 11

12 Multiscale Approach Gaussian pyramid representation is used to overcome computational complexity limitations The anomaly score is computed for every scale 12

13 Multiscale Anomaly Detection Using Diffusion Maps Anomaly score - level k + 1 Suspicious points after threshold Level k Anomaly score - level k 13

14 Multiscale Anomaly Detection Using Diffusion Maps Input image A l A l A 0 Output image 14

15 Multiscale Anomaly Detection Using Diffusion Maps - Example Original image Output 15

16 Proposed Sol. Spatio-Temporal (ST) Cb Detection Prev. image Flux calculation Curr. image 1 α Σ A l α Downsampling (scale l) S l S l S 0 Define new score - Cb score : S l = α Φ l + 1 α A l 16 Output image

17 Intermediate Results Original Image Spatial Anomaly Detection ST Output ST Cb Detection 17

18 Intermediate Results Spatial Anomaly Detection ST Cb Detection 18

19 Intermediate Results Original Image Spatial Anomaly Detection ST Output ST Cb Detection 19

20 Multiple Time-Step decision Image k-3 Image k-2 Image k-1 Image k Image k+1 Image k+2 ST Cb Detection ST Cb Detection ST Cb Detection Pixelwise OR Output k 20

21 Results Original Image ST Cb Detection Spatial Anomaly Detection Multiple time-step Decision 21

22 Results Spatial Anomaly Detection ST Cb Detection Multiple timestep Decision 22

23 Results 25

24 Conclusions Integrated temporal feature with diffusion maps based spatial anomaly detection Developed unsupervised spatio-temporal analysis framework for Cb clouds detection Multiple time-step decision improved detection rates significantly 26

25 Future Work More sophisticated Cb score Multiscale time approach Different ways to calculate flux Use of multiple modalities Dynamic threshold 27

26 Thank you! Questions? 28