A convolutional fully connected structure with hard sample memory pool for land use classification

Dong Xie; Arthur C. Depoian II; Lorenzo E. Jaques; Colleen P. Bailey

doi:10.1117/12.2588025

12 April 2021 A convolutional fully connected structure with hard sample memory pool for land use classification

Dong Xie, Arthur C. Depoian II, Lorenzo E. Jaques, Colleen P. Bailey

Proceedings Volume 11735, Pattern Recognition and Tracking XXXII; 117350E (2021) https://doi.org/10.1117/12.2588025
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

As one of the classic fields of computer vision, image classification has been booming with the improvement of chip performance and algorithm efficiency. With the rapid progress of deep learning in recent decades, remote sensing land cover and land use image classification has ushered in a golden period of development. This paper presents a new deep learning classifier to classify remote sensing land cover and land use images. The approach first uses multi-layer convolutional neural networks to extract the image features, attached through a fully-connected neural network to generate the sample loss. Then, a hard sample memory pool is created to collect the samples with large losses during the training. A batch of hard samples is randomly extracted from the memory pool to participate in the training of the convolutional fully connected model so that the model becomes more robust. Our method is validated by testing the classic remote sensing land cover and land use dataset. Compared with the previous popular classification algorithm, our algorithm can classify images more accurately with a shorter training iteration.

Conference Presentation

Citation Download Citation

Dong Xie, Arthur C. Depoian II, Lorenzo E. Jaques, and Colleen P. Bailey "A convolutional fully connected structure with hard sample memory pool for land use classification", Proc. SPIE 11735, Pattern Recognition and Tracking XXXII, 117350E (12 April 2021); https://doi.org/10.1117/12.2588025

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