In remote sensing analysis, automatic extraction of road network from satellite or aerial images can be a most needed approach for efficient road database creation, refinement, and updating.
A hyperspectral remote sensing images feature extraction method, i.e., discrete cosine transform (DCT) spectral regression discriminant analysis (SRDA) subspace method, was presented to solve the above problems.
Therefore, some methods such as scattered photon extraction package [5], remote phosphor package [6 8], and ring remote phosphor structure [9] are developed to better utilize the emitted photons and the design of optical lens.
These properties make the Maxwell system a better choice for DNA extractions at remote locations for the sample types tested.
It includes a feature extraction module with remote sensing image segmentation tools.
Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data.
This paper has important reference values for the development of similarly structured OBIA systems and line-involved extraction algorithms of remote sensing information.
According to the quantitative assessment method, building damage assessment mainly includes four steps: pre-disaster background information extraction, post-disaster remote sensing damage assessment of selected areas, field survey and calibration, and disaster index calculation by GIS.
More recently, methods based on morphological profiles are used for feature extraction from urban remote sensing data [36, 37] and find use in street tracking from SAR images [38].
As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing (RS) image has been a hot research topic in recent years.
