Hiperspektral görüntüler için değiştirilmiş SLIC tabanlı süperpiksel bölütleme

Year 2023, Volume: 38 Issue: 1, 399 - 408, 21.06.2022

İbrahim Onur Sığırcı Gokhan Bılgın

https://doi.org/10.17341/gazimmfd.835846

Abstract

Basit doğrusal iteratif kümeleme (SLIC) süperpiksel algoritması, bölütleme için verimli ve hızlı bir algoritmadır. Bu algoritma doğası gereği üç bantlı renkli görüntüler için tasarlanmıştır. Uzaktan algılamada yeni bir teknoloji olan hiperspektral görüntüleme, zengin spektral ve uzamsal bilgi taşıyan yüzlerce bant içermektedir. Bu çalışmada, SLIC algoritması hiperspektral görüntülerin yapısına göre değiştirilmiştir. Buna ek olarak, benzer süperpikseller DBSCAN (gürültülü uygulamaların yoğunluk tabanlı uzamsal kümelenmesi) algoritması ile birleştirilmiştir. Esinlenen yeni bir yaklaşımla süperpikseller arasındaki spektral benzerlik indeksi, evrensel görüntü kalitesi indeksine göre hesaplanmıştır. Elde edilen bölütleme haritalarının sınıflandırma performansına katkısı karşılaştırılmalı olarak sunulmuştur.

Keywords

Süperpiksel bölütleme, hiperspektral sınıflandırma, , Hyper-SLIC, spektral-uzamsal, görüntü kalite indeksi

Supporting Institution

Yıldız Teknik Üniversitesi - Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

2014-04-01-KAP01

Thanks

Bu çalışma Yıldız Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü Bölümü tarafından, 2014-04-01-KAP01 proje numarası ile desteklenmiştir.

A modified SLIC-based superpixel segmentation for hyperspectral images

Abstract

The SLIC (simple linear iterative clustering) superpixel algorithm is an efficient and fast algorithm for segmentation. The algorithm is inherently designed on three band color images. Hyperspectral imaging, which is a relatively new remote sensing technology, contains hundreds of bands which carry rich spectral and spatial information. In this study, the SLIC algorithm is modified according to the structure of hyperspectral images. In addition to that, the similar superpixels are merged with the DBSCAN (density-based spatial clustering of applications with noise) algorithm. As a novel inspired approach, the spectral similarity index between the superpixels are computed based on the universal image quality index. The contribution of the obtained segmentation maps to the classification performance is presented comparatively.

Keywords

Superpixel segmentation, , hiperspektral sınıflandırma, Hyper-SLIC, spectral-spatial, image quality index

Project Number

2014-04-01-KAP01

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