Wyszukiwarka
Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
Abstrakt
In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long Short-Term Memory (BiLSTM) method to reduce the overfitting problem and increase the performance of classification in change detection applications. Additionally, data usage and accuracy in remote sensing activities, particularly CD, can be significantly improved by a large number of training models based on BiLSTM. Normalization techniques are applied to input images in order to enhance the quality and reduce the difference in pixel value. The AlexNet and VGG16 models were used to extract useful features from the normalized images. The extracted features were then applied to the FWA-BiLSTM model, to give more weight to the unique features and increase the efficiency of classification. The attention layer selects the unique features that help to distinguish the changes in the remote sensing images. From the experimental results, it was clearly shown that the proposed FWA-BiLSTM model achieved better performance in terms of precision (93.43%), recall (93.16%), and overall accuracy (99.26%), when compared with the existing Difference-enhancement Dense-attention Convolutional Neural Network (DDCNN) model.
Cytowania
-
5 1
CrossRef
-
0
Web of Science
-
5 2
Scopus
Autorzy (5)
-
Raj Kumar Patra
-
Sujata N. Patil
-
Rachana Poongodan
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs14215402
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
-
Remote Sensing
nr 14,
ISSN: 2072-4292 - Język:
- angielski
- Rok wydania:
- 2022
- Opis bibliograficzny:
- Patra R. K., Patil S. N., Falkowski-Gilski P., Łubniewski Z., Poongodan R.: Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images// Remote Sensing -Vol. 14,iss. 21 (2022), s.5402-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs14215402
- Źródła finansowania:
-
- Działalność statutowa/subwencja
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 132 razy
Publikacje, które mogą cię zainteresować
Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection
- A. Stateczny,
- G. Uday Kiran,
- G. Bindu
- + 2 autorów
Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification
- A. Stateczny,
- S. M. Bolugallu,
- P. B. Divakarachari
- + 2 autorów
Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
- J. Sengupta,
- R. Alzbutas,
- P. Falkowski-Gilski
- + 1 autorów
Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function
- S. N. Shivappriya,
- M. J. P. Priyadarsini,
- A. Stateczny
- + 2 autorów