Urban land use classification from remotely sensed images has drawn great attention in the past decades. Most researchers derive land use data from remotely sensed images alone, but the results are not quite satisfying for detecting detailed land use classes in urban areas. Fuzzy urban land use classes proposed here consist of a number of fuzzy memberships that offer direct links to findings fr...

Traditional spectral classi®cation of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels. For some 30 years the spatial information inherent in remotely sensed images has been employed, albeit by a limited number of researchers, to enhance spectral classi®cation. This has been achieved primarily by ®...

As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towar...

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely ...

It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditional K-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing imag...

The present paper describes a feature extraction method based on -band wavelet packet frames for segmenting remotely sensed images. These wavelet features are then evaluated and selected using an efficient neurofuzzy algorithm. Both the feature extraction and neurofuzzy feature evaluation methods are unsupervised, and they do not require the knowledge of the number and distribution of classes c...

Methods for object detection from optical near-range photographs often have access to databases containing thousands or even more labelled images. These images are used to train machine-learning based approaches and to evaluate their performance. In contrast, remotely sensed data is often more difficult to obtain and to be labelled, in particular for sensors such as synthetic aperture radar (SA...

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V....