Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely
sensed data. Although significant progress has been made in image classification based …

Downscaling has an important role to play in remote sensing. It allows prediction at a finer
spatial resolution than that of the input imagery, based on either (i) assumptions or prior …

Hyperspectral images are well-known for their fine spectral resolution to discriminate
different materials. However, their spatial resolution is relatively low due to the trade-off in …

Straightforward classification using kernelized SVMs requires evaluating the kernel for a test
vector and each of the support vectors. For a class of kernels we show that one can do this …

Occurrence of mixed pixels in remote sensing images is a major problem particularly at
coarse spatial resolutions. Therefore, sub-pixel classification is often preferred, where a …

Soft classification techniques avoid the loss of information characteristic to hard
classification techniques when handling mixed pixels. Sub‐pixel map** is a method …

Hyperspectral images can easily discriminate different materials due to their fine spectral
resolution. However, obtaining a hyperspectral image (HSI) with a high spatial resolution is …

Map** the spatio-temporal characteristics of wetland inundation has an important
significance to the study of wetland environment and associated flora and fauna. High …

The mixed pixel is a common problem in remote sensing classification. Even though the
composition of these pixels for different classes can be estimated with a pixel un-mixing …

Long-term monitoring of shoreline changes is of significant importance for coastal erosion
prediction and coastal planning. This paper presents the use of the Landsat archival dataset …