INTRODUCTION Problem: Damage condition of residential areas are more concerned than that of natural...

INTRODUCTIONProblem: Damage condition of residential areas are more concerned than that of natural areas in post-hurricane damage assessment. Recognition of residential and natural areas from commonly used low-spatial-resolution hyperspectral images is thus important.

Solution: A spatial-feature extraction method based on hierarchical Fourier transform – Co-occurrence matrix is developed. Spatial and spectral features are then combined to a joint feature vector. Best feature combinations are determined by K-fold cross validation.

METHOD

Lin Cong, Brian Nutter, Daan LiangWind Science and Engineering

Department of Electrical and Computer EngineeringDepartment of Construction Engineering & Engineering Technology

Texas Tech University, Lubbock, TX, USAe-mail: {lin.cong, brian.nutter, daan.liang}@ttu.edu

Joint Solution of Urban Structure Detection from Hyperion Hyperspectral data

This material is based upon work supported by the National Science Foundation under Grant No. 0800487

Fourier Transform

Co-occurrence matrix

Texture measures

Spectral correlation

Feature selection

Bayes Classification

Hyperspectral data

PCA components

K-means clustering

PCA transform

Flow chart

Datasets

(a) (b) (d)(c)

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Figure 1. (a) Original hyperspectral image taken over Lubbock, TX in 01/2003; (b) – (c) The top two significant PCA bands of Lubbock dataset; (d) Spectral correlation against the spectrum of construction asphalt; (e) Original hyperspectral image taken over New Orleans, LA in 04/2005; (f) – (g) The top two significant PCA bands of New Orleans dataset; (h) Spectral correlation against the spectrum of construction asphalt;

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Figure 2. (a) A sample of residential region; (b) The Fourier transform of the residential region; (c) Plot of the directional energy distribution in Fourier domain; (d) Co-occurrence matrix calculated with the direction of the maximum energy and offset equal to one;

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Figure 3. (a) Another sample of residential region; (b) The Fourier transform of the residential region; (c) Plot of the directional energy distribution in Fourier domain; (d) Co-occurrence matrix calculated with the direction of the maximum energy and offset equal to one;

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Figure 4. (a) A sample of natural region; (b) The Fourier transform of the natural region; (c) Plot of the directional energy distribution in Fourier domain; (d) Co-occurrence matrix calculated with the direction of the maximum energy and offset equal to one;

(1) Contrast (CON)2

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Texture measures

CON DIS HOM SIM ASM MAX ENT

Figure 5. Texture measures of Lubbock dataset

CON DIS HOM SIM ASM MAX ENT

Figure 6. Texture measures of New Orleans dataset

Feature Selection

K-fold cross validation is applied on the training dataset to determine the best combinations of the spectral and spatial features.

Rank Combination Error Rank Combination Error Rank Combination Error 1 1100111001 1.88% 21 1111011000 2.17% 1004 0000100000 12.5%2 1111111001 1.90% 22 1101100000 2.18% 1005 0010000110 12.7%3 1101011001 1.97% 23 1010111001 2.19% 1006 0010000111 12.7%4 1101011000 2.01% 24 1011001001 2.21% 1007 0000000111 12.7%5 1101001001 2.02% 25 1011011001 2.21% 1008 0010100110 12.8%6 1101010001 2.04% 26 1111101001 2.21% 1009 0000000110 12.8%7 1110111001 2.05% 27 1011011000 2.23% 1010 0000100110 12.9%8 1101101000 2.07% 28 1011010001 2.23% 1011 0010100101 13.0%9 1111001001 2.08% 29 1111101000 2.24% 1012 0010000101 13.0%10 1111011001 2.10% 30 1111110001 2.25% 1013 0000100101 13.1%11 1011100000 2.10% 31 1001011001 2.26% 1014 0000000101 13.2%12 1101110000 2.11% 32 1111110000 2.26% 1015 0011000010 13.2%13 1001100000 2.13% 33 1111001000 2.27% 1016 0101000000 13.7%14 1100111000 2.13% 34 1111100001 2.28% 1017 0001000010 14.7%15 1111111000 2.13% 35 1111010000 2.28% 1018 0010000010 15.3%16 1101111000 2.15% 36 1101101001 2.29% 1019 0000000010 15.8%17 1111010001 2.15% 37 1111111010 2.30% 1020 0011000000 16.1%18 1110111000 2.16% 38 1101010000 2.31% 1021 0001000000 19.4%19 1101111001 2.16% 39 1111111011 2.31% 1022 0010000000 24.4%20 1101100001 2.17% 40 1111100000 2.31% 1023 0100000000 27.2%

Rank Combination Error Rank Combination Error Rank Combination Error 1 1110001001 5.67% 21 1111111001 6.60% 1004 0000100010 18.4%2 1110011001 5.77% 22 1111101000 6.63% 1005 1101000010 18.5%3 1110111000 5.81% 23 1111110001 6.66% 1006 1000100010 18.7%4 1110001000 5.84% 24 1111101001 6.68% 1007 0101000010 19.1%5 1110010001 5.84% 25 1110000001 6.75% 1008 0001000010 21.0%6 1110101000 5.86% 26 1111100001 6.80% 1009 1000000010 21.6%7 1110011000 5.87% 27 1111110000 6.83% 1010 1000100000 21.7%8 1110010000 5.89% 28 1111100000 6.93% 1011 1001000010 22.2%9 1110110000 5.96% 29 1111000001 7.12% 1012 1011000000 24.0%

10 1110101001 6.01% 30 1110011010 7.36% 1013 0011000000 24.6%11 1110110001 6.03% 31 1110101010 7.39% 1014 0100000000 26.4%12 1110111001 6.06% 32 0110111001 7.43% 1015 0101000000 26.5%13 1111011000 6.19% 33 1110110010 7.44% 1016 0000100000 26.6%14 1111001000 6.29% 34 1110001100 7.50% 1017 1010000000 27.0%15 1111001001 6.29% 35 1110111011 7.52% 1018 1100000000 27.0%16 1111011001 6.34% 36 1110101011 7.55% 1019 0001000000 27.7%17 1111010000 6.39% 37 1110111100 7.56% 1020 1101000000 28.1%18 1111111000 6.42% 38 0110011001 7.58% 1021 0010000000 28.9%19 1111010001 6.50% 39 1110111010 7.58% 1022 1001000000 31.8%20 1110100001 6.57% 40 0110001001 7.59% 1023 1000000000 39.8%

Table 1: Performance of a subset of all joint feature combinations for Lubbock dataset.Features are listed in the combinations following the order: PCA1, PCA2, spectral correlation, CON, DIS, HOM, SIM, ASM, MAX, ENT. A “1” means that the feature in the associated position is selected in the combination, and a “0” means that associated feature is not selected.

Table 2: Performance of a subset of all joint feature combinations for New Orleans dataset.

Bayes Classification

(a) Ground truth (d) Joint solution(c) Purely spatial(b) Purely spectral

Figure 7. Results of Bayes classification for Lubbock dataset. (a) Manually made ground truth; (b) – (d) Results by using purely spectral features, purely spatial features, joint features, respectively. Blue: residential areas correctly classified; Green: natural areas correctly classified; Red: residential areas misclassifed as natural areas; Pink: natural areas misclassifed as residential areas.

(a) Ground truth (d) Joint solution(c) Purely spatial(b) Purely spectral

Figure 8. Results of Bayes classification for New Orleans dataset. (a) Manually made ground truth; (b) – (d) Results by using purely spectral features, purely spatial features, joint features, respectively. Blue: residential areas correctly classified; Green: natural areas correctly classified; Red: residential areas misclassifed as natural areas; Pink: natural areas misclassifed as residential areas.

Spectral Solution(avg. error: 15.45%)

Residential Region Natural RegionClassified as Residential 50443 12281

Classified as Natural 8222 61734Error Rate 14.20% 16.59%

Spatial Solution(avg. error: 13.43%)


Classified as Natural 10346 66536Error Rate 21.26%

10.10%Joint Solution

(avg. error: 10.84%)Residential Region Natural Region

Classified as Residential 51127 6848Classified as Natural 7538 67168

Error Rate 12.85% 9.25%

Fourier transform – Co-occurrence matrix• Residential areas display periodic street patterns while the natural areas are universal.• Fourier Transform is applied to detect the directions orthogonal to the street patterns.• Gray level co-occurrence matrix is calculated between neighboring pixels with an offset of one in the direction orthogonal to the street patterns.

Results

Table 3: Error rates of the Bayes classification for Lubbock dataset


Residential Natural + RiverClassified as Residential 64609 15888

Classified as Natural or River 3106 25617

Error Rate 4.59% 38.28%




Error Rate 7.40% 38.83%Joint Solution

(avg. error: 12.99%)Residential Natural + River

Classified as Residential 65225 11699


Error Rate 3.68% 28.19%

Table 4: Error rates of the Bayes classification for New Orleans dataset

“Cross” Bayes Classification

1. Training data of New Orleans dataset is used to train the Bayes classifier, and then the Lubbock dataset is classified.

(c) Joint solution(b) Purely spatial(a) Purely spectral

Figure 9. “Cross” classification results of Lubbock dataset. Blue: residential areas correctly classified; Green: natural areas correctly classified; Red: residential areas misclassifed as natural areas; Pink: natural areas misclassifed as residential areas.



Classified as Natural 18451 4962Error Rate 31.35% 93.30%



Classified as Natural 3473 60408Error Rate 5.92%

18.38%Joint Solution

(avg. error: 20.25%)Residential Region Natural Region

Classified as Residential 56524 24721Classified as Natural 2141 49294

Error Rate 3.65% 33.40%

Table 5: Error rates of the “cross” classification for Lubbock dataset

(c) Joint solution(b) Purely spatial(a) Purely spectral

Figure 10. “Cross” classification results of New Orleans dataset. Blue: residential areas correctly classified; Green: natural areas correctly classified; Red: residential areas misclassifed as natural areas; Pink: natural areas misclassifed as residential areas.




Error Rate 8.62% 96.64%




Error Rate 20.49% 14.45%Joint Solution

(avg. error: 18.20%)Residential Natural + River

Classified as Residential 53937 6079


Error Rate 20.35% 14.65%

Table 6: Error rates of the “cross” classification for New Orleans dataset

2. Training data of Lubbock dataset is used to train the Bayes classifier, and then the New Orleans dataset is classified.

Conclusion1. Improved accuracy in Bayes classification between residential

and natural areas was achieved by using both spectral and macroscopic spatial information.

2. The spatial features extracted by proposed Fourier transform – Co-occurrence matrix method seem to be reliable in “cross” classification, although the purely spectral information between different datasets is so different that it fails the cross classification.

Future work1. More testing and verification on additional datasets are needed

in the future. 2. The segmentations of residential and natural areas can be used

for model choice in spectral unmixing.3. The spectral unmixing results at the same position before and

after a hurricane can be compared to assess the damage level.

mailto:brian.nutter%[email protected]





INTRODUCTION Problem: Damage condition of residential areas are more concerned than that of natural...

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