Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

In recent years, technological advances have led to the increasing use of unmanned aerial
vehicles (UAVs) for forestry applications. One emerging field for drone application is forest …

In recent years, remote-sensing scene classification is one of the research hotspots and has
played an important role in the field of intelligent interpretation of remote-sensing data …

Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

Abstract Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has attracted much attention due to low annotation costs. Existing methods often rely on …

Emerging technologies such as Internet of Things (IoT) can provide significant potential in
Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time …

The revolutionary capabilities of large language models (LLMs) have paved the way for
multimodal large language models (MLLMs) and fostered diverse applications across …

The advances in remote sensing sensors during the last two decades have led to the
production of very high spatial resolution multispectral images. In order to adapt to this rapid …

It is necessary to improve the ecological environment and keep ecological balance of nature
reserves that have particularly important function on precious and endangered wildlife …

Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …