In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusio...

Remote-sensing reflectance (R(rs)), which is defined as the ratio of water-leaving radiance (L(w)) to downwelling irradiance just above the surface (E(d)(0⁺)), varies with both water constituents (including bottom properties of optically-shallow waters) and angular geometry. L(w) is commonly measured in the field or by satellite sensors at convenient angles, while E(d)(0⁺) can be measured in th...

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing da...

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared refl...

In general only a small part of spatial data in SDI change because of different factors, so rapid and effective updating methods are very vital. With the improvement of spatial resolution of satellite remote sensing (RS) imagery, it is possible to update spatial data infrastructure efficiently by satellite RS images. But the RS data volume is vast, so high processing capacity is required. It is...

Remote sensing has been widely used for mapping land cover and is considered key to monitoring changes in forest areas in the REDD+ Measurement, Reporting and Verification (MRV) system. But Remote Sensing as a desk study cannot capture the whole picture; it also requires ground checking. Therefore, complementing remote sensing analysis using participatory mapping can help provide information fo...

Geospatial Technology is one of the three emerging technologies in the 21st century. Remote sensing images typically contain a combination of pure and mixed pixels. Mixed pixels result when the sensor's instantaneous field-of-view (IFOV) includes more than one land cover class on the ground. The phenomenon of this "mixed pixels" caused great difficulties to remote sensing image classification. ...

Because of the extreme importance of remote sensing as a data input to GIS, it has. Remote sensing can be defined as any process whereby information is.Remote sensing is the process of acquiring datainformation about. Application G-The final element of the remote sensing process is achieved when we.Abstract : Remote sensing is a technique to observe the earth surface or the atmosphere from. Pro...