Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

This study compared five widely used globally gridded biomass burning emissions inventories for the 2002-2011 period (Global Fire Emissions Database 3 (GFED3), Global Fire Emissions Database 4 (GFED4), Global Fire Assimilation System 1.0 (GFAS1.0), Fire INventory from NCAR 1.0 (FINN1.0) and Global Inventory for Chemistry-Climate studies-GFED4 (G-G)). Average annual CO2 emissions range from 6521.3 to 9661.5 Tg year(-1) for five inventories, with extensive amounts in Africa, South America and Southeast Asia. Coefficient of Variation for Southern America, Northern and Southern Africa are 30%, 39% and 48%. Globally, the majority of CO2 emissions are released from savanna burnings, followed by forest and cropland burnings. The largest differences among the five inventories are mainly attributable to the overestimation of CO2 emissions by FINN1.0 in Southeast Asia savanna and cropland burning, and underestimation in Southern Africa savanna and Amazon forest burning. The overestimation in Africa by G-G also contributes to the differences.