SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION
Remote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2019-10-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/315/2019/isprs-archives-XLII-4-W18-315-2019.pdf |
id |
doaj-992f952586654f8792a59fb93214570a |
---|---|
record_format |
Article |
spelling |
doaj-992f952586654f8792a59fb93214570a2020-11-25T01:15:36ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-10-01XLII-4-W1831532010.5194/isprs-archives-XLII-4-W18-315-2019SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATIONM. Dowlatshah0H. Ghassemian1M. Imani2Image Processing and Information Analysis Laboratory, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranImage Processing and Information Analysis Laboratory, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranImage Processing and Information Analysis Laboratory, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranRemote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images; and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/315/2019/isprs-archives-XLII-4-W18-315-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Dowlatshah H. Ghassemian M. Imani |
spellingShingle |
M. Dowlatshah H. Ghassemian M. Imani SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
M. Dowlatshah H. Ghassemian M. Imani |
author_sort |
M. Dowlatshah |
title |
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION |
title_short |
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION |
title_full |
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION |
title_fullStr |
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION |
title_full_unstemmed |
SPATIAL-SPECTRAL MORPHOLOGICAL FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES CLASSIFICATION |
title_sort |
spatial-spectral morphological feature extraction for hyperspectral images classification |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2019-10-01 |
description |
Remote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images; and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/315/2019/isprs-archives-XLII-4-W18-315-2019.pdf |
work_keys_str_mv |
AT mdowlatshah spatialspectralmorphologicalfeatureextractionforhyperspectralimagesclassification AT hghassemian spatialspectralmorphologicalfeatureextractionforhyperspectralimagesclassification AT mimani spatialspectralmorphologicalfeatureextractionforhyperspectralimagesclassification |
_version_ |
1725152373061451776 |