Abstract
Up to the present, an enormous number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. As a consequence, identifying the same materials from spatially different scenes or positions can be difficult. In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral datasets (Houston2013 and Houston2018) demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. The codes will be available from the website: https://sites.google.com/view/danfeng-hong/data-code.
Highlights
L AND use and land cover (LULC) classification has been playing an increasingly vital role in the high-level image interpretation and analysis of remote sensing [1], [2]
1) Evaluation Metrics: Pixel-wise classification is explored as a potential application for quantitatively evaluating the performance of these feature extraction algorithms
2) State-of-the-Art Comparison in Related Works: The morphological profiles (MPs)-based or Attribute profile (AP)-based methods we investigate in this article are obviously categorized into unsupervised feature extraction
Summary
L AND use and land cover (LULC) classification has been playing an increasingly vital role in the high-level image interpretation and analysis of remote sensing [1], [2]. In [19], mathematical morphology has shown its superiority in modeling and extracting the spatial information of an image related to the geometric shape and scale of different objects. Based on this concept, Pesaresi and Benediktsson [20] developed morphological profiles (MPs) to segment high-resolution satellite imagery by applying a sequence of opening and closing operators to reconstruct or connect the targeted objects with a size-increasing structurized element (SE). Fauvel et al [23] designed a novel strategy of jointly
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