225 Abstract Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification. 1. Introduction Hyperspectral imagery (HSI) captures a dense spectral sampling of reflectance values over a wide range of spectrum [1]. This rich spectral information provides additional capacities for many remote sensing applications including environmental monitoring, crop analysis, plant and mineral exploration, etc. In conventional HSI classification approaches, only spectral signatures of every pixel in the image are considered. Classification techniques use spectral values alone to assign labels to each pixel are so-called pixel- wise classifiers [2]. However, the spatial context information in hyperspectral images is also useful for scene interpretation. During the last decade, there has been a great deal of effort in exploiting spatial features to improve HSI classification performance. In [3], a volumetric gray level co-occurrence matrix was used to extract the texture features of hyperspectral images. In [4], a spectral-spatial preprocessing method was proposed to incorporate spatial features for HSI classification by employing a multihypothesis prediction strategy that was developed for compressed sensing image reconstruction [5] and image super-resolution [6]. A 3-D discrete wavelet transform (3-D DWT) was employed in [7] to effectively capture the spatial information of hyperspectral images in different scales and orientations. 2-D Gabor filters were applied to selected bands or principal components of the hyperspectral image to extract Gabor texture features for classification [8, 9]. Morphological profiles (MPs) generated via a series of structural elements were introduced in [10] to capture multiscale structural features for HSI classification. Due to the effectiveness of MPs characterizing spatial structural features, many features based MPs have been proposed for HSI classification, such as extended morphological profiles (EMPs) [11], attributes profiles (APs) [12], and extended multi-attribute profile (EMAP) [13]. In [14], local binary patterns (LBPs) and Gabor texture features were combined to enhance the discriminative power of the spatial features. Spatial feature extraction plays a key role in improving the HSI classification performance. In this paper, we introduce the gradient local auto-correlations (GLAC) [15] and present a new spatial feature extraction method for hyperspectral images using GLAC. The GLAC descriptor, which is based on the second order of statistics of gradients (spatial and orientational auto-correlations of local image gradients), can effectively capture rich information from images and has been successfully used in motion recognition [22] and human detection [15, 23]. To our best knowledge, this is the first time, image gradient based features have been used for hyperspectral image classification. Experimental results on two HSI datasets demonstrate the effectiveness of the proposed feature extraction method compared with several state-of- the-art spatial feature extraction methods for HSI classification. The remainder of this paper is organized as follows. Section 2 describes the details of the GLAC descriptor and the classification framework. Section 3 presents the experimental results with two real hyperspectral datasets. Finally, Section 4 concludes the paper. 2. Methodology 2.1. Gradient local auto-correlations GLAC [15] descriptor is an effective tool for extracting shift-invariant image features. Let I be an image region Hyperspectral Image Classification Using Gradient Local Auto-Correlations Chen Chen 1 , Junjun Jiang 2 , Baochang Zhang 3 , Wankou Yang 4 , Jianzhong Guo 5 1. Department of Electrical Engineering, University of Texas at Dallas, Texas, USA 2. School of Computer Science, China University of Geosciences, Wuhan, China 3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China 4. School of Automation, Southeast University, Nanjing, China 5. School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, China chenchen870713@.gmail.com 1