The thriving development of earth observation technology makes more and high-resolution remote-sensing images easy to obtain. However, caused by fine-resolution, the huge spatial spectral complexity leads automation semantic segmentation becoming a challenging task. Addressing such an issue represents exciting research field, which paves way for scene-level landscape pattern analysis decision-m...

Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive...

* Corresponding author Automated generation of maps from remotely sensed images remains an unsolved problem in the quest for bringing together image analysis and GIS technologies. This paper proposes a framework for an automatic scene interpretation system with the objective of populating/updating a GIS database. The proposed approach exploits semantic contextual information, combining scene an...

In this Letter, an unsupervised algorithm for detecting changes in multi-spectral and multi-temporal remotely-sensed images is presented. Such an algorithm makes it possible to reduce the eVects of r̀egistration noise’ on the accuracy of change detection. In addition, it can be used to reduce the typologies of detected changes in order to better locate the changes under investigation.

Detection of edges in multispectral images has been a challenging task in the research community over the past few years. In this work, a novel vector-based approach is adopted for edge detection in multichannel remotely sensed images. The discontinuity between homogeneous regions in the image is detected using the image density value estimated at the mean vector of the sliding window. The prop...

Compact characterization of soil moisture at a given scale using self-organizing feature maps is presented. We find that as few as 49 neurons capture the spatial structure of remotely sensed soil moisture images from the southern Great Plains. Average latent heat flux computed from the original image of 21 204 pixels and from 49 neurons are comparable.

Abs t r ac t . This paper addresses the problem of motion estimation in sequences of remotely sensed images of the sea. When the temporal sampling period is low the estimation of the velocity field can be done by finding the correspondence between structures detected in the images. The scale space aproximation of these structures using the wavelet multiressolution is presented. The corresponden...

Remotely sensed images have already attained an important role in a wide spectrum of tasks ranging from weather forecasting to battlefield reconnaissance. One of the most promising remote sensing technologies is the imaging radar, known as synthetic aperture radar (SAR). SAR overcomes the nighttime limitations of optical cameras, and the cloud-cover limitations of both optical and infrared imag...

Remotely sensed data is the most important data source for environmental change study over the past 40 years. Since large collections of remote sensing imagery have been acquired in a time frame of successive years, it is now possible to study long-term spatio-temporal pattern of environmental change and impacts of human activities. This study seeks an efficient and practical methodology for la...