Integrating Spectral and Spatial features for Hyperspectral Image Classification with a Modified Composite Kernel Framework

  • Shashi Ranjan
  • Jignesh N. SarvaiyaEmail author
  • Jigisha N. Patel
Original Article


This paper presents two modified composite kernel frameworks for the hyperspectral image classification. The first proposed framework is a simple extension of the previously proposed generalized composite kernel (GCK) approach, and in the second framework, we have used a voting strategy. The developed modified composite kernels combine different spatial and spectral profiles without considering any weight parameter. We have used the multinomial logistic regression (MLR) algorithm for the classification of hyperspectral images in this work. We have developed different spatial profiles: extended morphological profile (EMP) and extended multiattribute profile (EMAP) in this work, and further integrated them with the spectral profile for spectral-spatial classification. Experimental results with the AVIRIS Indian Pines, ROSIS Pavia university area and AVIRIS Salinas images indicate that the proposed framework leads to an improvement in classification accuracy. The obtained overall accuracy increases by 2–3% for AVIRIS Indian Pines scene, 1–2% for ROSIS Pavia University Area scene and 1–3% for AVIRIS Salinas scene with the first proposed framework. In case of the second proposed framework with a sufficient number of training samples per class, the overall accuracy increases by 3–7% for AVIRIS Indian Pines scene, 2.5% for ROSIS Pavia University Area scene and 7–8% for AVIRIS Salinas scene. For this framework (Modified-CKV with MLR) we have obtained the overall accuracy of 96.95% for the Indian Pines scene, 99.33% for the Pavia University Area, and 97.76% for the Salinas scene with 75 training samples per class, which is less than 2% of the available labelled samples. With the results obtained, we have also concluded that with the linear combination approach for the construction of composite kernels, the overall accuracy improves without any increase in computational complexity.


Image classification Hyperspectral images Extended morphological profile Extended multiattribute profile Generalized composite kernel Multinomial logistic regression 


Integration von spektralen und räumlichen Merkmalen für die Klassifizierung von hyperspektralen Bildern mit einem modifizierten Composite Kernel Framework. Dieser Artikel stellt zwei modifizierte Composite-Kernel-Frameworks für die hyperspektrale Bildklassifikation vor. Das erste vorgeschlagene Konzept ist eine einfache Erweiterung des bereits verfügbaren generalisierten Composite-Kernel (GCK) Ansatzes, während wir beim zweiten eine Voting Strategy verwendet haben. Die entwickelten modifizierten Composite-Kernels verknüpfen unterschiedliche räumliche und spektrale Profile ohne Berücksichtigung von Gewichtungen. Wir haben dafür den Multinomial Logistic Regression (MLR) Algorithmus für die Klassifizierung der hyperspektralen Bilddaten verwendet und unterschiedliche räumliche Profile entwickelt – das Erweiterte Morphologische Profil (EMP) und das Erweiterte Multiattributive Profil (EMAP) – und diese mit spektralen Profilen für die spektral-räumliche Klassifizierung integriert. Experimentelle Ergebnisse mit den Test-Szenen Indian Pines (AVIRIS), Universitätsgelände Pavia (ROSIS) und Salinas (AVIRIS) zeigen, dass das vorgeschlagene Framework zu einer Verbesserung der Klassifizierungsgenauigkeit führt. Der erzielte Gesamtgenauigkeit steigt um 2 – 3% für die Szene Indian Pines (AVIRIS), 1 – 2% für die Szene Universitätsgelände Pavia (ROSIS) und 1 – 3% für die Szene Salinas (AVIRIS). Im Falle des zweiten vorgeschlagenen Frameworks und einer ausreichenden Anzahl von Trainingsgebieten für jede Klasse erhöht sich die Gesamtgenauigkeit um 3–7% für die Szene Indian Pines (AVIRIS), 2 – 5% für die Szene Universitätsgelände Pavia (ROSIS) und 7 – 8% für die Szene Salinas (AVIRIS). Für dieses Framework (Modified-CKV mit MLR) haben wir die Gesamtgenauigkeit von 96,95% für die Szene Indian Pines, 99,33% für die Szene Universitätsgelände Pavia und 97,76% für die Salinas-Szene mit 75 Trainingsproben pro Klasse erhalten, was weniger als 2% der verfügbaren Samples entspricht. Die Ergebnisse zeigen, dass sich durch Linearkombination von Composite Kernels die Gesamtgenauigkeit erhöht, ohne dass dadurch die Komplexität der Berechnungen steigt.


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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  1. 1.Department of Electronics EngineeringSVNIT SuratSuratIndia

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