Remote sensing change detection has played an important role in many applications. Most traditional change detection methods deal with single-band or multispectral remote sensing images. Hyperspectral remote sensing images offer more detailed information on spectral changes so as to present promising change detection performance. The…
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Table of contents
Presents the table of contents for this issue of the periodical.…
Adaptive Window Size Estimation in Unsupervised Change Detection
Many problems related to change detection require to compute image features on local windows. Such features usually combine in each pixel locations spectral values (luminance) associated with some spatial properties, such as texture features or more advanced local relationships between pixels. Therefore, as far as…
Characterising Reedbeds Using LiDAR Data: Potential and Limitations
Reedbeds are dominated by a small number of plant species, but are extremely valuable habitats for faunal biodiversity. However, reedbeds often exist in small patches distributed across landscapes and for most regions there is a lack of information about their location and condition. This paper…
[Front cover]
Presents the front cover for this issue of the publication.…
Characterising Reedbeds Using LiDAR Data: Potential and Limitations
Reedbeds are dominated by a small number of plant species, but are extremely valuable habitats for faunal biodiversity. However, reedbeds often exist in small patches distributed across landscapes and for most regions there is a lack of information about their location and condition. This paper…
Retrieval of Forest Biomass From ALOS PALSAR Data Using a Lookup Table Method
Mapping of forest biomass over large area and in higher accuracy becomes more and more important for researches on global carbon cycle and climate change. The feasibility and problems of forest biomass estimations based on lookup table (LUT) methods using ALOS PALSAR data are investigated…
An Improved FCM Algorithm Based on the SVDD for Unsupervised Hyperspectral Data Classification
Unsupervised classification approaches, also known as “clustering algorithms”, can be considered a solution to problems associated with the supervised classification of remotely sensed image data. The most important of these problems with respect to statistical classification algorithms is the lack of enough high quality training…