Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

By using ionosonde data recorded at Chiang Mai (18.8° N, 98.9° E, magnetic latitude is 9.1° N), Puer (22.7° N, 101.1° E, magnetic latitude is 12.9° N), and Leshan (29.6° N, 103.7° E, magnetic latitude is 19.8° N), the statistical features of different types of spread F (SF) occurrence at low and middle latitudes were analyzed in this study. The results showed that the SF occurrence had obvious local time, latitude, and SF-type variations. The range spread F (RSF) occurrence in equinox months decreased with the increase in latitude, while the frequency spread F’s (FSF) occurrence rate in the summer months increased and the onset time of FSF became earlier when the latitude increased. The generation of SF depends on the SF type. A plasma bubble excited by the generalized Rayleigh–Taylor instability (GRT) at the equator is more likely to produce RSF, while nighttime medium-scale traveling ionospheric disturbances (MSTIDs) induced by Perkins instability at middle latitudes is the main reason for the generation of FSF.