AN EFFICIENT FRAMEWORK FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES IN URBAN AREAS

Davood Akbari1, Saeid Homayouni2, Abdolreza Safari1, Safa Khazai3

1S Dept. of Surveying and Geomatics Eng. , College of Engineering, University of Tehran, Tehran, Iran

2Department of Geography, University of Ottawa, Ottawa, Canada 3Dept. Civil Eng. , College of Engineering, Imam Hussein Comprehensive University, Tehran, Iran

ABSTRACT

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spectral-spatial classification has been proposed using Minimum Spanning Forest (MSF) grown from automatically selected markers. This paper aims at improving this approach for classification of hyperspectral images in urban areas. The proposed framework is based on deriving the optimal spatial and spectral features of the original hyperspectral image using the Principal Component Analysis (PCA). The spatial features extracted in this study are wavelet, Gabor filter, mean, contrast, entropy, variance, homogeneity, dissimilarity, second moment, and correlation. The experimental results on three hyperspectral datasets demonstrate that compared to the original MSF-based approach, the proposed framework yields more accurate classification maps.

Index Terms — Hyperspectral images, Spectral-spatial Classification, Marker selection, Minimum Spanning Forest.

1. INTRODUCTION

Hyperspectral imagery has been widely investigated for land-cover classification due to its broad coverage of wavelength and high spectral sampling rate. However, the classification of hyperspectral images is a very challenging task because of the very high dimensionality of the pixels and the small number of labeled samples available for training procedure. There are two major approaches for classification of hyperspectral images: the spectral or pixel-based and the spectral-spatial approaches. While the pixel-based techniques such as Support Vector Machines (SVM) use only the spectral information of the pixels, the spectral-spatial frameworks like the Minimum Spanning Forest (MSF) imply both the spectral characteristics and the spatial context of the image pixels [1]. In urban areas, many researches have demonstrated that the use of spectral-spatial information improves the classification results compared to the use spectral data alone [2].

Recently, an effective approach for spectral-spatial classification has been proposed using the MSF grown from automatically selected markers. In this framework, proposed by Tarabalka et al. [3], a connected components labeling is applied on the classification map. Then, if a region is large enough, its marker is determined as the P% of pixels within this region with the highest probability estimates. Otherwise, if a region is small, it should lead to a marker only if it is very reliable; potential marker is formed by the pixels with probability estimates higher than a

defined threshold. Tarabalka et al. [3] used the SVM pixel-wise classification to specify the most reliable classified pixels as markers. Then a MSF, which is rooted on the selected markers, was constructed. However, this approach does not use the optimal spectral bands for improving the classification accuracies. On the other hand, it has been recently proposed that spatial/texture analysis may significantly improve the classification results using a much reduced number of spectral bands [2]. Hence, this paper aims at improving this approach based on deriving the optimal spatial and spectral features of the hyperspectral image. In this study, the optimal spatial and spectral features are selected using the PCA algorithm. Moreover, the MSF-based approach grown of markers is used to classify the hyperspectral images.

2. MSF-BASED FRAMEWORK

The MSF framework grown of markers is used to segment and classify. In MSF, each pixel is considered as a vertex νϵV of an undirected graph G = (V, E, W), where V and E are sets of vertices and edges, respectively, and W is a mapping of the set of the edges E into R . Each edge e , ∈ E of this graph connects a couple of vertices i and j corresponding to the neighboring pixels. Furthermore, a weight w , is assigned to each edge e , , which indicates the degree of dissimilarity between two vertices (i.e., two corresponding pixels) connected by this edge. We used an eight neighborhood and the Spectral Angle Mapper (SAM) measure for computing the weights of edges, as described in [4]. Given a graph G = (V, E, W), the MSF rooted on a set of 𝑚 distinct vertices {𝑡 , … , 𝑡 } consists in finding a spanning forest 𝐹∗ = (𝑉, 𝐸 ∗) of 𝐺, such that each distinct tree of 𝐹∗is grown from one root 𝑡 , and the sum of the edges' weights of 𝐹∗ is minimal [5].

In order to obtain the MSF rooted on markers, m additional vertices t , i = 1, … , m are introduced. Each extra vertex t is connected by the edge with a null weight to the pixels representing a marker 𝑖. Furthermore, an additional root vertex 𝑟 is added and is connected by the null-weight edges to the vertices t (see Fig. 1).

Fig 1. An example of addition of extra vertices t , t and r to the image graph for the construction of an MSF rooted on markers 1 and 2; non-marker pixels are denoted by “0.”

The minimal spanning tree of the constructed graph induces a

MSF in G, where each tree is grown on a vertex t . Finally, a spectral-spatial classification map is obtained by assigning the class of each marker to all the pixels grown from this marker.

3. PROPOSED FRAMEWORK

The flowchart of the proposed approach is presented in Fig. 2. As shown, the proposed approach contains four main steps: i) spatial features extraction, ii) optimization of spectral and spatial features, iii) marker selection, and iv) segmentation and classification.

In this study, ten usual texture features including wavelet, Gabor filter, mean, variance, homogeneity, contrast, entropy, dissimilarity, second moment, and correlation [6] are used to define the spatial information of the image pixels. The PCA method is used to extract the optimal spectral and spatial features. We used PCA to retain 95% of the total variance in the data. Then, the different combinations of optimal spatial features with the optimal spectral characteristics were classified using marker-based MSF algorithm. Based on obtained results, the combination of five features, wavelet, Gabor filter, mean, contrast and entropy, with spectral characteristics of hyperspectral image to achieve optimal results. These features are summarized in Table 1.

Fig 2. Scheme of the proposed framework

Table 1. All used features and their formulation

Description Formula Name Type 𝜓 , (𝑥)= |𝑎| 𝜓(𝑥 − 𝑏𝑎 ) 𝜓(𝑡)𝑑𝑥 = 0

where 𝑎 and 𝑏 determine the frequency region and the time of signal, respectively.

(𝑊) (𝑎, 𝑏) =< 𝑓(𝑥), 𝜓 , (𝑥) >= 𝑓(𝑥)𝜓 , (𝑥)𝑑𝑥

Wavelet Transform [7]

Structural Textures 𝑚 and 𝑛 are the

indices for the scale and the orientate, respectively. The 𝑢 is the central frequency of the scale.

𝑓 (𝑥, 𝑦) = 12𝜋𝜎 exp {− 𝑥 + 𝑦2𝜎 } × cos (2𝜋(𝑢 𝑥𝑐𝑜𝑠𝜃+ 𝑢 𝑦𝑠𝑖𝑛𝜃 )) 𝑓 (𝑥, 𝑦) = 12𝜋𝜎 exp {− 𝑥 + 𝑦2𝜎 } × sin (2𝜋(𝑢 𝑥𝑐𝑜𝑠𝜃+ 𝑢 𝑦𝑠𝑖𝑛𝜃 ))

Gabor Transform

[8]

𝑃 (𝑖, 𝑗) corresponds to the number of occurrences of the pixel values (i, j) which are a distance d apart in original image.

.

𝜇 = 𝑖(𝑃 (𝑖, 𝑗))𝜇 = 𝑗(𝑃 (𝑖, 𝑗)) Mean

[9]

Statistical Textures

(First order) − 𝑃 (𝑖, 𝑗)𝑙𝑜𝑔𝑃 (𝑖, 𝑗) Entropy [9] (𝑖 − 𝑗) 𝑃 (𝑖, 𝑗) Contrast

[10]

Statistical Textures (Second order)

4. EXPERIMENTAL RESULTS

Table 2 describes the main characteristics of datasets used in this work. For each of the classes in each dataset, we randomly choose around 10% of the labeled samples for training and the rest are used for testing.

After optimization of spectral and spatial features using PCA algorithm, the resulting image for Berlin, Pavia, and Washington DC Mall datasets consists of 61, 64 and 54 features, respectively (see Table 3). To create a map of markers, a pixel-wise classification was performed, using the multiclass SVM classifier with the Gaussian Radial Basis (RBF) kernel function. The penalty parameter C and (spread of the RBF kernel) are chosen by a five-fold cross validation technique. The optimal values obtained are C=256 and 01.0 for Berlin dataset, C=128 and 𝛾 = 2 for Pavia dataset and C=128 and 𝛾 = 0.1 for Washington DC Mall dataset. Furthermore, each connected component (using 8-neighborhood connectivity) of the SVM classification map is analyzed. In each dataset, if it contains more than 20 pixels, 5% of its pixels with the highest probability estimates were selected as the

marker [3]. Otherwise, the region marker is formed by the pixels with probability estimates higher than a threshold 𝜏. The threshold 𝜏 is equal to the lowest probability within the highest 2% of the probability estimates for the whole image. In order to evaluate the results of the proposed framework, we have implemented MSF algorithm on the original hyperspectral images. Fig. 3 shows the color representation, reference data and the classification maps of the original and the proposed MSF-based frameworks for each dataset. As can be seen in this figure, the classification maps obtained by proposed framework contain many more homogeneous regions compared to those obtained by the original MSF-based approach.

The results obtained of the overall accuracy (OA) and Kappa coefficient (κ) parameters are reported in Table 4. As can be seen in this table, for the Berlin and Pavia datasets, the proposed approach achieves an approximately 4% OA higher than the original MSF framework. Moreover, in the Washington DC Mall dataset, the proposed approach is a slight increase of about 0.7% OA higher than the original MSF framework.

Table 2. The main characteristics of used datasets

Dataset Sensor Spatial dimension

(pixels) Spatial resolution

(m/pixel) Spectral resolution

(bands) Berlin HyMap 300 300 3.5 114 Pavia ROSIS-03 610×340 1.3 103

Washington DC Mall HYDICE 1208×307 1.5 191

Table 3. The number of spectral and spatial features for the datasets used Dataset spectral bands wavelet Gabor filter mean contrast entropy resulting image Berlin 14 12 10 8 9 8 61 Pavia 17 15 13 8 5 6 64

Washington DC Mall 15 11 14 4 5 5 54

Classification map Legend Reference map Color representation Dataset Proposed MSF Original MSF

vegetation build-up impervious soil water

Berlin

Asphalt Meadows Gravel Trees Metal Sheets Bare Soil Bitumen Bricks Shadows

Pavia

Shadow Trees Grass Water Road Roafs Trails

Washington

DC Mall

Fig 3. The color representations and the reference maps of the datasets used, and the corresponding classification maps obtained for the original MSF-based, and the proposed MSF-based frameworks.

Table 4. The values of global accuracy parameters obtained for the three datasets used.

Original MSF

Proposed MSF Dataset

86.07 90.28 OA (%) Berlin 81.16 86.47 κ (%) 94.39 98.80 OA (%) Pavia 92.62 98.41 κ (%) 87.20 87.92 OA (%) Washington

DC Mall 83.79 84.65 κ (%) Fig. 4 also shows graphs related to the producer's accuracy rate

of each class for three datasets. As can be seen in this figure, in Berlin and Pavia datasets, all the class-specific accuracy rates for the proposed MSF are higher than accuracy rates for the original MSF framework. In other dataset, there is not a significant difference in accuracy between the two methods.

5. CONCLUSION

In this paper, an improved framework, based on the spectral-spatial information for classification of urban hyperspectral images, was proposed. The approach is based on deriving the optimal spatial and

spectral characteristics or features from original hyperspectral image. In this work, ten usual texture features are used to define the spatial information of the image pixels. Moreover, these optimal spectral and spatial features were selected using PCA algorithm. Based on the obtained results, the combination of five spatial features, wavelet, Gabor filter, mean, contrast and entropy, with spectral features of hyperspectral image achieves the optimal results. These optimal features are then classified using the marker-based MSF classifier. Experimental results on three hyperspectral images have demonstrated that the proposed method improves classification accuracy rates compared to the original MSF. For the Berlin and Pavia datasets, the proposed approach achieves an approximately 4% OA higher than the original MSF framework. Moreover, in the Washington DC Mall dataset, the proposed approach is a slight increase of about 0.7% OA higher than the original MSF framework.

6. ACKNOLEDGEMENT

The authors would like to thank Prof. P. Gamba from the University of Pavia, Italy, and Prof. D. Landgrebe from Purdue University, U.S., for providing the hyperspectral data.

(a) (b) (c)

Fig 4. The producer’s accuracy of the classes for (a) Berlin, (b) Pavia and (c) Washington DC Mall datasets.

7. REFERENCES

[1] M. Fauvel, J. Chanussot, J. A. Benediktsson, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 46, pp. 3804–3814, 2008.

[2] O. Rajadell, P. Garc´ıa-Sevilla, and F. Pla, "Textural features for hyperspectral pixel classification," in IbPria09, Lecture Notes in Computer Science 5524, pp. 208-216, 2009.

[3] Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, pp. 1267–1279, 2010.

[4] F. van der Meer, "The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery," Int. J. Appl. Earth Observation Geoinformation, vol. 8, pp. 3–17, 2006.

[5] J. Stawiaski, "Mathematical morphology and graphs: Application to interactive medical image segmentation," Ph.D. dissertation, Paris School Mines, Paris, France, 2008.

[6] S. Selvarajah and S. R. Kodituwakku, "Analysis and Comparison of Texture Features for Content Based Image Retrieval," International Journal of Latest Trends in Computing, vol. 2, 2011.

[7] S. Mallat, "A Wavelet Tour of Signal Processing," Academic Press, San Diego, 1999.

[8] G. Shaw and D. Manolakis, "Signal processing for hyperspectral image explotation," IEEE Signal Process. Mag., vol. 19, p. 12, 2002.

[9] R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE Trans. on Systems, Man, and Cybernetics, vol. SMC-3, pp. 610-621, 1973.

[10] R. Gonzalez and R. Woods, "Digital Image Processing," 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.

7580859095

100

Cla

ss A

ccur

acy

(%)

Original MSF Proposed MSF

75

80

85

90

95

100

7580859095

100