Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence

D. E. Turan; E. Aptoula; A. Erturk; G. Taskin

doi:10.1109/IGARSS52108.2023.10282341

Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence

D. E. Turan^*
, E. Aptoula
, A. Erturk
, G. Taskin

^*Bu çalışma için yazışmadan sorumlu yazar

Afet Yönetimi Enstitüsü

Sabanci University
Kocaeli University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

7 Atıf (Scopus)

Özet

This study addresses the explainability challenges of deep-learning models in the context of hyperspectral remote sensing image classification. Three prominent explainable artificial intelligence methods, namely GradCAM, GradCAM++, and Guided Backpropagation, have been employed in order to comprehend the decision-making process of a typical convolutional neural network model during spatial-spectral hyperspectral image classification. The experiments that have been conducted investigate the impact of pixel patch sizes on spatial attention, as well as spectral band importance. The findings provide insights into the behavior of both convolutional neural networks, as well as the comparative performance of explainability techniques.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	5950-5953
Sayfa sayısı	4
ISBN (Elektronik)	9798350320107
DOI'lar	https://doi.org/10.1109/IGARSS52108.2023.10282341
Yayın durumu	Yayınlandı - 2023
Etkinlik	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Süre: 16 Tem 2023 → 21 Tem 2023

Yayın serisi

Adı	International Geoscience and Remote Sensing Symposium (IGARSS)
Hacim	2023-July
ISSN (Elektronik)	2153-6996

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Ülke/Bölge	United States
Şehir	Pasadena
Periyot	16/07/23 → 21/07/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Belgeye Erişim

10.1109/IGARSS52108.2023.10282341

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Turan, D. E., Aptoula, E., Erturk, A., & Taskin, G. (2023). Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 5950-5953). (International Geoscience and Remote Sensing Symposium (IGARSS) ; Hac. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10282341

Turan, D. E. ; Aptoula, E. ; Erturk, A. et al. / Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 5950-5953 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

@inproceedings{42749acdb71147f8833560dbe0957f33,

title = "Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence",

abstract = "This study addresses the explainability challenges of deep-learning models in the context of hyperspectral remote sensing image classification. Three prominent explainable artificial intelligence methods, namely GradCAM, GradCAM++, and Guided Backpropagation, have been employed in order to comprehend the decision-making process of a typical convolutional neural network model during spatial-spectral hyperspectral image classification. The experiments that have been conducted investigate the impact of pixel patch sizes on spatial attention, as well as spectral band importance. The findings provide insights into the behavior of both convolutional neural networks, as well as the comparative performance of explainability techniques.",

keywords = "Explainable artificial intelligence, GradCam, guided backpropagation, hyperspectral images, interpretability",

author = "Turan, \{D. E.\} and E. Aptoula and A. Erturk and G. Taskin",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",

year = "2023",

doi = "10.1109/IGARSS52108.2023.10282341",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5950--5953",

booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

address = "United States",

}

Turan, DE, Aptoula, E, Erturk, A & Taskin, G 2023, Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS) , hac. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 5950-5953, 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282341

Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence. / Turan, D. E.; Aptoula, E.; Erturk, A. et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5950-5953 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Hac. 2023-July).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence

AU - Turan, D. E.

AU - Aptoula, E.

AU - Erturk, A.

AU - Taskin, G.

PY - 2023

Y1 - 2023

N2 - This study addresses the explainability challenges of deep-learning models in the context of hyperspectral remote sensing image classification. Three prominent explainable artificial intelligence methods, namely GradCAM, GradCAM++, and Guided Backpropagation, have been employed in order to comprehend the decision-making process of a typical convolutional neural network model during spatial-spectral hyperspectral image classification. The experiments that have been conducted investigate the impact of pixel patch sizes on spatial attention, as well as spectral band importance. The findings provide insights into the behavior of both convolutional neural networks, as well as the comparative performance of explainability techniques.

AB - This study addresses the explainability challenges of deep-learning models in the context of hyperspectral remote sensing image classification. Three prominent explainable artificial intelligence methods, namely GradCAM, GradCAM++, and Guided Backpropagation, have been employed in order to comprehend the decision-making process of a typical convolutional neural network model during spatial-spectral hyperspectral image classification. The experiments that have been conducted investigate the impact of pixel patch sizes on spatial attention, as well as spectral band importance. The findings provide insights into the behavior of both convolutional neural networks, as well as the comparative performance of explainability techniques.

KW - Explainable artificial intelligence

KW - GradCam

KW - guided backpropagation

KW - hyperspectral images

KW - interpretability

UR - https://www.scopus.com/pages/publications/85178383641

U2 - 10.1109/IGARSS52108.2023.10282341

DO - 10.1109/IGARSS52108.2023.10282341

M3 - Conference contribution

AN - SCOPUS:85178383641

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 5950

EP - 5953

BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023

Y2 - 16 July 2023 through 21 July 2023

ER -

Turan DE, Aptoula E, Erturk A, Taskin G. Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 5950-5953. (International Geoscience and Remote Sensing Symposium (IGARSS) ). doi: 10.1109/IGARSS52108.2023.10282341

Interpreting Hyperspectral Remote Sensing Image Classification Methods Via Explainable Artificial Intelligence

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap