This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against...

An object-oriented change detection algorithm was proposed and experimented to multi-temporal ALOS remotely sensed images. In contrast with conventional pixel-based algorithms, this approach processed homogenous blocks generated by object-oriented image segmentation for change detection. Similar pixels were merged into homogeneous objects by image segmentation at first, and each object (polygon...

The paper presents a comparison of the principal lossy compression algorithms, Vector Quantization (VQ), JPEG and Wavelets (WV) posterior KLT applied to multispectral remotely sensed images and evaluated by the classification algorithm KNN. The main goal of the compression of remotely sensed images is a reduction of the huge requirements for downlink and storage. The Karhunen Loeve Transform fi...

Advanced data mining technologies and the large quantities of Remotely Sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data, can be of importance in precision agriculture, community planning, resource discovery and other areas. However, in most cases the ima...

Multilayer Perceptrons (MLPs) have been proven to be an effective way to solve classification tasks. A major concern in their use is the difficulty to define the proper network for a specific application, due to the sensitivity to the initial conditions and overfitting and underfitting problems which limit their generalization capability. Moreover, time and hardware constraints may seriously re...

Extending on the method of regression-class mixture decomposition (RCMD), a RCMD-based feature mining model with genetic algorithm (coined RFMM-GA) is proposed in this paper for the extraction of features in complex remotely sensed images with a large proportion of noise. Within the framework of RFMM-GA, different features in the feature space correspond to different components of a mixture in ...

The international scientific context is pointing out the role that can be played by models and observation systems for the evaluation and forecasting of the risks related to environmental problems. In this context, coupling data and models becomes a scientific challenge regarding the following considerations: A numerical model without observation data is not really interesting; On the other han...

Studies in the area of Pattern Recognition have indicated that in most cases a classifier performs differently from one pattern class to another. This observation gave birth to the idea of combining the individual results from different classifiers to derive a consensus decision. This work investigates the potential of combining neural networks to remotely sensed images. A classifier system is ...

Classification of Images is one of the challenging tasks in image analysis. Image classification is used in many fields such as Remote sensing, medical diagnosis, robotics, etc. Classification is to identify homogeneous groups of data points in a given dataset and assigning it to a class. In this paper classes of image objects are to be classified as region or area of interest for the land use/...

Remotely sensed imagery data from satellites and airborne platforms have become important tools to assess vulnerability of urban areas and to grasp damage distribution due to natural disasters. The platform and sensors of remote sensing should be selected considering the area to cover, urgency, weather and time conditions, and resolution of images. Satellites with optical and/or SAR sensors can...