Results 21 to 30 of about 8,937 (214)
Remote Sensing, 2022 Semantic segmentation of very-high-resolution (VHR) remote sensing images plays an important role in the intelligent interpretation of remote sensing since it predicts pixel-level labels to the images.
Bo Liu +4 more
doaj +1 more source
Recurrent Multiresolution Convolutional Networks for VHR Image Classification [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2018 Classification of very high resolution (VHR) satellite images has three major challenges: 1) inherent low intra-class and high inter-class spectral similarities, 2) mismatching resolution of available bands, and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of image
John Ray Bergado +2 more
openaire +2 more sources
Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery [PDF]
, 2014 Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the ...
Aguilar Torres, Fernando José +3 more
core +3 more sources
Remote Sensing, 2021 Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS ...
Luis Gonzaga García-Montero +6 more
doaj +1 more source
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery [PDF]
, 2018 Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management.
A Newell +11 more
core +3 more sources
A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 This article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages.
Sicong Liu +5 more
openaire +3 more sources
Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series [PDF]
, 2016 Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat
Aguilar Torres, Fernando José +4 more
core +8 more sources
Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study [PDF]
, 2014 The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking
Charlier, Johannes +8 more
core +2 more sources
Sensors, 2019 Observing sea ice by very high-resolution (VHR) images not only improves the quality of lower-resolution remote sensing products (e.g., sea ice concentration, distribution of melt ponds and pressure ridges, sea ice surface roughness, etc.) by providing ...
Chang-Uk Hyun +3 more
doaj +1 more source
IEEE Access, 2020 Rotation-invariant feature extraction is crucial for object detection in Very High Resolution (VHR) optical remote sensing images. Although convolutional neural networks (CNNs) are good at extracting the translation-invariant features and have been ...
Zhi Zhang +4 more
doaj +1 more source