Wetlands are valuable natural resources that provide many benefits to the environment.
Therefore, mapping wetlands is crucially important. Several review papers on remote …

With the significant progress of urbanization, cities and towns are suffering from air pollution,
heat island effects, and other environmental problems. Urban vegetation, especially trees …

The development of robust object-based classification methods suitable for medium to high
resolution satellite imagery provides a valid alternative to 'traditional'pixel-based methods …

Deep learning has already been proved as a powerful state-of-the-art technique for many
image understanding tasks in computer vision and other applications including remote …

Wildlife management is based on various measurements representative of the health of
populations and their habitats. Some agencies are focusing on animal surveys to manage …

The purpose of this study is to examine the use of multi-resolution object-based
classification methods for the classification of Unmanned Aircraft Systems (UAS) images of …

The exploitation of new high revisit frequency satellite observations is an important
opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri …

Pixel-based (PB) and geographic-object-based (GEOBIA) classification approaches allow
the extraction of different objects from multispectral images (MS). The primary goal of this …

Crop supply and management is a global issue, particularly in the context of global climate
change and rising urbanization. Accurate mapping and monitoring of specific crop types are …

Deep convolutional neural network (DCNN) requires massive training datasets to trigger its
image classification power, while collecting training samples for remote sensing application …