Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a
large number of domains, including railways. In this paper, we present a systematic literature …

Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …

An accurate identification of objects from the acquisition system depends on the clear
segmentation and classification of remote sensing images. With the limited financial …

Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …

In this article, we propose an efficient and effective framework to fuse hyperspectral and light
detection and ranging (LiDAR) data using two coupled convolutional neural networks …

The monitoring of coastal wetlands is of great importance to the protection of marine and
terrestrial ecosystems. However, due to the complex environment, severe vegetation …

Convolutional neural networks (CNNs) have attracted increasing attention in the field of
multimodal cooperation. Recently, the adoption of CNN-based methods has achieved …

Due to rich spectral and spatial information, the combination of hyperspectral and
multispectral images (MSIs) has been widely used for Earth observation, such as wetland …

Remote sensing is one of the most common ways to extract relevant information about Earth
and our environment. Remote sensing acquisitions can be done by both active (synthetic …