Magnusson, M. 2006. Evaluation of remote sensing techniques for estimation of forest variables at stand level. Doctor's dissertation. There is a continuous need for accurate forest description. Forest data at stand level is required in forestry planning, in particular when scheduling treatments within the next few years. Collection of forest data is typically acquired with subjective surveying ...

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristic...

Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications. However,...

Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers U-Net design skip connected asymmetric-convolution-based (MACU-Net), usi...

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on ...