Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

Kačan, Marin; Turčinović, Filip; Bojanjac, Dario; Bosiljevac, Marko

doi:10.3390/rs14225673

Pregled bibliografske jedinice broj: 1246988

Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

Kačan, Marin; Turčinović, Filip; Bojanjac, Dario; Bosiljevac, Marko

Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data // Remote sensing, 14 (2022), 22; 5673, 27 doi:10.3390/rs14225673 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1246988 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

Autori
Kačan, Marin ; Turčinović, Filip ; Bojanjac, Dario ; Bosiljevac, Marko

Izvornik
Remote sensing (2072-4292) 14 (2022), 22; 5673, 27

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
object classification ; radar image reconstruction ; convolutional neural networks ; ResNet18 ; GBSAR ; Omega-K algorithm

Sažetak
The availability of low-cost microwave components today enables the development of various high- frequency sensors and radars, including Ground- based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Interdisciplinarne tehničke znanosti

POVEZANOST RADA

Projekti:
HRZZ-IP-2019-04-1064 - Pametna platforma za daljinska istraživanja u okolišu i industriji primjenom milimetarskih valova (MMSENSE) (Bosiljevac, Marko, HRZZ ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Marko Bosiljevac (autor)