Classification Techniques with Applications in Remote Sensing
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1 Classification Techniques with Applications in Remote Sensing Hunter Glanz California Polytechnic State University San Luis Obispo November 1, 2017 Glanz Land Cover Classification November 1, / 44
2 Outline 1 2 Traditional Classification 3 Our Approach 4 Results Glanz Land Cover Classification November 1, / 44
3 Classification One of the most common forms of statistical inference: Classification Glanz Land Cover Classification November 1, / 44
4 Classification One of the most common forms of statistical inference: Classification The problem of identifying to which of a set of categories a new observation belongs, on the basis of training data. Glanz Land Cover Classification November 1, / 44
5 Think Way Back Glanz Land Cover Classification November 1, / 44
6 Think Way Back Learning shapes! Glanz Land Cover Classification November 1, / 44
7 Is It a Triangle? Glanz Land Cover Classification November 1, / 44
8 Is It a Triangle? Establish a decision rule. Glanz Land Cover Classification November 1, / 44
9 Wait Just a Minute...again Glanz Land Cover Classification November 1, / 44
10 Wait Just a Minute...again Glanz Land Cover Classification November 1, / 44
11 Could he be... (a) waving hello. Glanz Land Cover Classification November 1, / 44
12 Could he be... (a) waving hello. (b) waving goodbye. Glanz Land Cover Classification November 1, / 44
13 Could he be... (a) waving hello. (b) waving goodbye. (c) trying to get our attention (Hey! Look over here). Glanz Land Cover Classification November 1, / 44
14 Could he be... (a) waving hello. (b) waving goodbye. (c) trying to get our attention (Hey! Look over here). (d) impersonating someone riding on a parade float. Glanz Land Cover Classification November 1, / 44
15 Could he be... (a) waving hello. (b) waving goodbye. (c) trying to get our attention (Hey! Look over here). (d) impersonating someone riding on a parade float. (e) waiting for a high-five. Glanz Land Cover Classification November 1, / 44
16 Could he be... (a) waving hello. (b) waving goodbye. (c) trying to get our attention (Hey! Look over here). (d) impersonating someone riding on a parade float. (e) waiting for a high-five. (f) trying to hail a cab. Glanz Land Cover Classification November 1, / 44
17 Could he be... (a) waving hello. (b) waving goodbye. (c) trying to get our attention (Hey! Look over here). (d) impersonating someone riding on a parade float. (e) waiting for a high-five. (f) trying to hail a cab. (g) doing something else entirely. Glanz Land Cover Classification November 1, / 44
18 More Examples spam filters. Antivirus/Computer Security software. Speech/text recognition. Signal processing. Sound clip whale call whale species. Image segmentation. Glanz Land Cover Classification November 1, / 44
19 Land Cover Classification Inform models of biosphere-atmosphere interaction and global carbon cycle. Improve understanding of anthropomorphic effects. Improve land use management. Improve natural resource management. Glanz Land Cover Classification November 1, / 44
20 Distinguishing Water Glanz Land Cover Classification November 1, / 44
21 Distinguishing Water Glanz Land Cover Classification November 1, / 44
22 Ideal Data Glanz Land Cover Classification November 1, / 44
23 Compute the Means Wetness Darkness Glanz Land Cover Classification November 1, / 44
24 Compute the Covariance Wetness Darkness Glanz Land Cover Classification November 1, / 44
25 How Do We Classify a New Observation? Wetness Darkness Glanz Land Cover Classification November 1, / 44
26 Use Some Measure of Closeness Wetness Darkness Glanz Land Cover Classification November 1, / 44
27 Decision Boundary Wetness Darkness Glanz Land Cover Classification November 1, / 44
28 Less Ideal Data Wetness Darkness Glanz Land Cover Classification November 1, / 44
29 Decision Boundary Wetness Darkness Glanz Land Cover Classification November 1, / 44
30 Still Ideal? Wetness Darkness Glanz Land Cover Classification November 1, / 44
31 Decision Boundary Wetness Darkness Glanz Land Cover Classification November 1, / 44
32 Challenging, but Fun Wetness Darkness Glanz Land Cover Classification November 1, / 44
33 Decision Boundary Wetness Darkness Glanz Land Cover Classification November 1, / 44
34 The Ultimate Goal Land Cover Classification Glanz Land Cover Classification November 1, / 44
35 Land Cover Classes Glanz Land Cover Classification November 1, / 44
36 The Result Glanz Land Cover Classification November 1, / 44
37 Mission Data: multitemporal, multispectral data from MODIS instrument Task: Land-cover classification (and change detection) Terra and Aqua satellites: Glanz Land Cover Classification November 1, / 44
38 Output - Small Subset Glanz Land Cover Classification November 1, / 44
39 Data and Issues In one year, pixel v data: 7 spectral bands (surface reflectance) 8-day weekly observations (45 time points) We use: All 7 spectral bands Middle 28 time points for the year Data quality and missing data are a huge issue! Glanz Land Cover Classification November 1, / 44
40 Traditional Classification Outline 1 2 Traditional Classification 3 Our Approach 4 Results Glanz Land Cover Classification November 1, / 44
41 Traditional Classification How Do You Perform Classification? 1 A regression-type method (Multinomial) Logistic Regression 2 LDA? QDA? 3 Random Forests 4 Neural Networks What s our beef with these methods? Glanz Land Cover Classification November 1, / 44
42 Traditional Classification What Does The Application of These Methods Look Like? Glanz Land Cover Classification November 1, / 44
43 Our Approach Outline 1 2 Traditional Classification 3 Our Approach 4 Results Glanz Land Cover Classification November 1, / 44
44 Our Approach We Propose the Matrix Normal Model Original Multivariate Normal Model: X v θ v = c N(vec(µ c ), σ c ) Decompose Σ c into spectral and temporal pieces: Σ s and Σ t,c. X v θ v = c MN(µ c, Σ s, Σ t,c ) vec(x v ) θ v = c N(vec(µ c ), Σ t,c Σ s ) Target Σ s with Principal Component Analysis (PCA) for dimension reduction. Glanz Land Cover Classification November 1, / 44
45 Our Approach Our Approach to Reality Missing data due to clouds and snow We estimate µ c, Σ s and Σ t,c in the presence of missing data, in a fancy way (Expectation-Maximization; The EM Algorithm). Then perform PCA on ˆΣ s 1 Reduce dimensionality 2 Preserve temporal information Glanz Land Cover Classification November 1, / 44
46 Our Approach A Glimpse at the Data Northeast NA Training Data Greenness Darkness Glanz Land Cover Classification November 1, / 44
47 Our Approach Small Example Glanz Land Cover Classification November 1, / 44
48 Our Approach Exploit Spatial Correlation (and Use Bayesian Statistics) Glanz Land Cover Classification November 1, / 44
49 Results Outline 1 2 Traditional Classification 3 Our Approach 4 Results Glanz Land Cover Classification November 1, / 44
50 Results Application to Montreal Area Glanz Land Cover Classification November 1, / 44
51 Results Tune the Emphasis on Spatial Relationships Glanz Land Cover Classification November 1, / 44
52 Results One (more) of the Difficulties Mixed Forests: Lands dominated by trees with a percent canopy cover > 60% and height exceeding 2 meters. Consists of tree communities with interspersed mixtures or mosaics of the other four forest cover types. None of the forest types exceeds 60% of landscape. Woody Savannas: Lands with herbaceous and other understorey systems, and with forest canopy cover between 30-60%. The forest cover height exceeds 2 meters. Glanz Land Cover Classification November 1, / 44
53 Results Conclusions MODIS data highly complex and messy Pros and cons to traditional methods Matrix Normal Model and graphical model appropriate Satisfying accuracy, verifiable results More physically interpretable parameters/results Glanz Land Cover Classification November 1, / 44
54 Results Thank You Questions? Glanz Land Cover Classification November 1, / 44
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