 Abstract— Satellite Remote sensing with repetitive and pan viewing and multispectral capabilities, is a powerful tool for mapping and monitoring the ecological changes. Analysis of the remote sensing data is faced with a number of challenges ranging from type of sensors, number of sensors, spectral responses of satellite sensors, resolutions in different domains and qualitative and quantitati...

Satellite imageries are provided in both panchromatic and multispectral images. The former have higher spatial resolutions while the latter have relatively lower spatial resolutions but are rich in spectral information. The two types of images have complementary features of spatial and spectral resolutions as such could be fused or merged to produce a better imagery that would be used in land u...

Classifying the remote sensing images requires a deeper understanding of imagery, machine learning classification algorithms, and profound insight into satellite images’ know-how properties. In this paper, convolutional neural network (CNN) is designed to classify multispectral SAT-4 four classes: trees, grassland, barren land, others. an airborne dataset that captures in 4 bands (R, G, B, infr...

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spe...

The article presents results of object oriented classification of Landsat ETM+ satellite image using fused multispectral and panchromatic data. Channels 2, 3 and 4 of the ETM+ image have been fused by applying PanSharp automatic algorithm (PCI Geomatica software) which preserves spectral characteristics of multispectral channels. Object oriented classification has been performed using eCognitio...

In this paper, we present a system that models the land-mobile satellite communication channel with a Markov chain approach supported by images of the mobile Earth station’s (MES) environment. Our method employs a video camera with a fish-eye lens capturing image sequences of the user environment from the position of the moving MES, image and signal processing algorithms to recognize propagatio...

A major satellite DNA sequence was isolated from the cat genome and its sequencing data revealed homology to the FA-SAT family. In situ hybridization of the cat satellite DNA and telomeric sequences to cat chromosomes, together with staining of constitutive heterochromatin, allowed the physical mapping of the FA-SAT sequences, and also an overall constitutive heterochromatin study in cat chromo...

An overview of the ‘triangle’ method for estimating soil surface wetness and evapotranspiration fraction from satellite imagery is presented here. The method is insensitive to initial atmospheric and surface conditions, net radiation and atmospheric correction, yet can yield accuracies comparable to other methods. We describe the method first from the standpoint of the how the triangle is obser...

X-SAT is a mini-satellite developed by the Satellite Engineering Centre of the Nanyang Technological University at Singapore. The focus of the technologydriven mission is the high-resolution remote sensing of the Southeast Asian region for environmental monitoring. To achieve the ambitious mission objectives, the GPS-based X-SAT Navigation System (XNS) will provide high-precision onboard orbit ...