Analysis of patch and sample size effects for 2D-3D CNN models using multiplatform dataset: hyperspectral image classification of ROSIS and Jilin-1 GP01 imagery

Abstract

Modern hyperspectral sensors provide a huge volume of data at spectral and spatial domains with high redundancy, which requires robust methods for analysis. In this study, 2D and 3D CNN models were applied to hyperspectral image datasets (ROSIS and Jilin-1 GP01) using varying patch and sample sizes to determine their combined impacts on the performance of deep learning models. Differences in classification performances in relation to particle and sample sizes were statistically analysed using McNemar’s test. According to the findings, raising the patch and sample size enhances the performance of the 2D/3D CNN model and produces more accurate results in the classification of hyperspectral imagery. To be more specific, the thematic maps produced with 400 and 600 samples resulted in a notable increase in overall accuracies (approximately 10%) compared to 50 and 100 samples per class. Statistical test results confirmed the importance of patch size selection for the case of limited training data and the maximum number of samples required for deep learning. It could be also mentioned that an appropriate patch and sample size should be selected considering the characteristics of the dataset and the type of deep learning algorithms.