This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1 Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery Almabrok Essa, Student Member, IEEE, Paheding Sidike, and Vijayan Asari, Senior Member, IEEE Abstract—In this letter, we propose to use an enhanced version of volumetric directional pattern to efficiently extract rich spatial context information in the hyperspectral imagery (HSI). The proposed technique fuses the texture information from three consecutive bands in the input HSI. The extracted local image texture features for each pixel of interest are then fed into an extreme learning machine classifier to assign object category. The experimental results on three standard hyperspectral data sets demonstrate the effectiveness of the proposed method for HSI classification compared with that of a set of state-of-the-art spatial extraction methods. Index Terms— Extreme learning machine (ELM), feature extraction, hyperspectral imagery (HSI), volumetric directional pattern (VDP). I. I NTRODUCTION T HE objective of hyperspectral imagery (HSI) classifica- tion is to assign each pixel in HSI into a class that it belongs to, which also termed thematic mapping. The key of each classification task is to utilize feature extraction tech- niques that must be able to extract pertinent features, which are most capable of preserving object class separability under different conditions during the image acquisition process. Spatial information has shown significant contribution for hyperspectral image classification. Over the last decades, a great deal of HSI classification schemes that use spatial features have been proposed in [2]–[4]. In [2], spatial structural features were generated for HSI classification using morpho- logical profile (MP). Its improved versions were developed due to its successful performance, such as extended MP (EMP) [3] and extended multiattribute profile (EMAP) [4]. In [5], HSI image features are extracted by effectively utilizing band sub- set averaging base image fusion and recursive filtering (IFRF), which outperforms EMP-based feature extractor for HSI classification. Chen et al. [6] effectively utilized the edge- computation-based approach, where spatial and rotational autocorrelations of local image gradients are obtained by gra- dient local autocorrelations [7]. Texture information is another useful factor that can aid in HSI classification. One of the most successful texture descriptors is Gabor feature [8], [9]. Manuscript received December 17, 2016; revised March 1, 2017 and April 7, 2017; accepted April 12, 2017. (Corresponding author: Almabrok Essa.) A. Essa and V. Asari are with the Department of Electrical and Com- puter Engineering, University of Dayton, Dayton, OH 45469 USA (e-mail: essaa1@udayton.edu; vasari1@udayton.edu). P. Sidike is with the Center for Sustainability, Saint Louis University, St. Louis, MO 63108 USA (e-mail: pahedings@slu.edu). Color versions of one or more of the figures in this letter are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2017.2695559 In [8], 2-D Gabor features were generated to capture the spatial information of hyperspectral images in different scales and orientations. Bau et al. [9] introduced the 3-D Gabor filters model for spectral–spatial information. It generates a set of features that captures specific-orientation-, scale-, and wavelength-dependent properties of an HSI image region. One of the most high performing texture algorithms based on the concept of local pattern descriptor is local binary pattern (LBP) [10], [11]. LBP has been applied for extracting spatial texture features in HSI classification [12] and it yields significantly better results compared with the other spatial- feature-based HSI classification techniques. In this method, the LBP code image is generated for each band in the input HSI. To describe the spatial characteristics of the pixel, the LBP histogram for each pixel of interest is computed with its corresponding neighborhood region. However, this method did not consider the texture features from the magnitude component of the image local differences as well as the local features from multiresolution of the image. Therefore, Sidike et al. [13] introduced a new spatial-feature-based HSI classification framework, which computes CLBP with multiple scales. In their work, local structural components contain the difference signs (i.e., original LBP) and the difference magnitudes are combined to obtain rich textural information. Furthermore, multiscale analysis in CLBP was used to further improve the classification accuracy. In this letter, we propose to modify the spatial feature extraction method, named volumetric direction pattern (VDP) technique, to extract the texture information from HSI. Unlike the techniques that are mentioned above, VDP extracts the texture features from the directional magnitude component of three consecutive bands in HSI, to describe the spatial characteristics of the pixel of interest from each band. Then a histogram is built to newly represent target pixel as a 1-D vector using its corresponding texture features. In pixelwise classification stage, an extreme learning machine (ELM) [14] is employed due to its efficient computation and promising classification performance [15], [16]. Experimental results show promising performance of modified VDP for the HSI classification task. The rest of the letter is organized as follows. In Section II, a detailed description of mathematical formation of the improved VDP technique is provided. Then the HSI classifi- cation framework using modified VDP and ELM is illustrated in Section III. We describe the data sets used in the exper- iments and then present performance evaluation as well as the comparison with state-of-the-art methods in Section IV. Finally, conclusions are drawn in Section V. 1545-598X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.