Support vector machine (SVM) is one of the most successful classifiers for remote sensing
image classification. However, the performance of SVM is mainly dependent on its …

In hyperspectral image (HSI) analysis, label information is a scarce resource and it is
unavoidably affected by human and nonhuman factors, resulting in a large amount of label …

Improving the geo-localization of optical satellite images is an important pre-processing step
for many remote sensing tasks like monitoring by image time series or scene analysis after …

Mapping different land cover types with satellite remote sensing data is significant for
restoring and protecting natural resources and ecological services in coastal wetlands. In …

Deep convolutional neural networks (CNN) have led to a successful breakthrough for
hyperspectral image (HSI) classification. In this paper, a CNN system embedded with an …

In recent years, deep learning has attracted an increasing amount of attention in machine
learning and artificial intelligence areas. Currently, many deep learning network-related …

Accurate ground-based cloud image categorization is a critical but challenging task that has
not been well addressed. One of the essential issues that affect the performance is to extract …

This article proposes a multiscale spectral features graph fusion (MSFGF) method for
selecting proper hyperspectral bands. The MSFGF regards that the selected bands should …

In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed,
trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …