Fuzzy methods in remote sensing have received growing interest for their particular value in situations where the geographical phenomena are inherently fuzzy. A fuzzy approach is investigated for the classi® cation of sub-urban land cover from remote sensing imagery and the evaluation of classi® cation accuracy. Under the fuzzy strategy, fuzziness, intrinsic to both remotely sensed data and gro...

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth’s interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth’s stethoscope. In a similar manner remote sensing is the rock hammer of the planetary sc...

A weakly supervised change detection method is proposed for remotely sensed multi-temporal images, by utilizing a Siamese neural network architecture. The architecture of the combination two multi-filter multi-scale deep convolutional networks (MFMS DCNN). Initially, trained image-level semantic labels image pairs in dataset. features are obtained using to generate difference (DI). Then, PCA an...

This presentation will discuss statistical methods for use in the analysis of remotely sensed data. Many of the methods represent enhancements of traditional means for processing remotely sensed data that have been developed by the CSIRO Mathematical and Information Sciences Remote Sesning and Monitoring Group (CMIS-RSM). The presentation will discuss statistical methods in the context of parti...

The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measur...

A general framework for change detection (CD) is proposed to analyze multi-modal remotely sensed data utilizing the Kronecker product between two representations (vectors or matrices). The method sensor independent and provides comparable results techniques that exist specific sensors. fusion technique a pixel-level approach incorporates inputs from different modalities, rendering enriched repr...

Analysis of the multi–spectral remotely sensed images of the areas destructed by an earthquake is proved to be a helpful tool for construction assessments. In this paper, we propose a new fast and reliable fuzzy change detection method for multi–spectral images. The proposed fuzzy change detection method is mathematically and experimentally investigated and shown to be efficient and effective.

Drought is a major environmental disaster in many parts of the world. Knowledge about the timing, severity and extentof drought can aid planning and decision-making. Drought indices derived from in-situ meteorological data have coarsespatial and temporal resolutions, thus, obtaining a real-time drought condition over a large area is difficult. This studyused advanced very high resolution radiom...

Clustering is unsupervised classification of patterns into groups. Neural networks are very useful tools for performing clustering. In this paper, we propose a new model for using artificial neural networks to perform clustering tasks on remotely sensed imagery. This model generates self-organizing maps (SOM) based on remotely sensed imagery and such related data as yield, nitrate, and moisture...