Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is suitable for a large variety of EO based value adding products and services, including: (i) low level image enhancement applications, such as automatic MS image topographic correction, co registration, mosaicking and compositing, (ii) high level MS image land cover (LC) and LC change (LCC) classification and (iii) content based image storage/retrieval in massive multi source EO image databases (big data mining).
Code (0)
등록된 구현이 없습니다.
Tasks
Earth ObservationImage EnhancementRetrievalShadow DetectionSimilar Papers 제목 키워드 기반
SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints
Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatia…
Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion
Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Satellite imaginary data are in increasingly higher resolution, both spatially and temporally, …
Weather ForecastingPoint upsampling networks for single-photon sensing
Single-photon sensing has generated great interest as a prominent technique of long-distance and ultra-sensitive imaging, however, it tends to yield sparse and spatially biased point clouds, thus limiting its practical u…
Point CloudsMSCloudCAM: Multi-Scale Context Adaptation with Convolutional Cross-Attention for Multispectral Cloud Segmentation
Clouds remain a major obstacle in optical satellite imaging, limiting accurate environmental and climate analysis. To address the strong spectral variability and the large scale differences among cloud types, we propose …
3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues
Classification and segmentation of 3D point clouds are important tasks in computer vision. Because of the irregular nature of point clouds, most of the existing methods convert point clouds into regular 3D voxel grids be…
SegmentationSemantic Segmentation