The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 479–486, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-479-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 479–486, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-479-2020

  21 Aug 2020

21 Aug 2020

EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

H. Teffahi1 and N. Teffahi2 H. Teffahi and N. Teffahi
  • 1ASAL, Algerian Space Agency, Space Application Center (CAS), Bouzareah, Algiers, Algeria
  • 2National Superior School of Marine Science and Coastal Planning, Dely Ibrahim, Algiers, Algeria

Keywords: Extended Multi-Attribute Profiles (EMAP), Dense Conventional Neural Network (DCNN), Spectral-spatial Classification, Deep Learning (DL), Mathematical Morphology, Feature Extraction and Fusion, Hyperspectral Remote Sensing Image classification

Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sensing images for thematic mapping purposes such as agricultural mapping, urban mapping, emergency mapping in case of natural disasters... In recent years, mathematical morphology and deep learning (DL) have been recognized as prominent feature extraction techniques that led to remarkable spectral-spatial classification performances. Among them, Extended Multi-Attribute Profiles (EMAP) and Dense Convolutional Neural Network (DCNN) are considered as robust and powerful approaches such as the work in this paper is based on these two techniques for the feature extraction stage and used in two combined manners and constructing the EMAP-DCNN frame. The experiments were conducted on two popular datasets: “Indian Pines” and “Huston” hyperspectral datasets. Experimental results demonstrate that the two proposed approaches of the EMAP-DCNN frame denoted EMAP-DCNN 1, EMAP-DCNN 2 provide competitive performances compared with some state-of-the-art spectral-spatial classification methods based on deep learning.