An important approach to unsupervised pixel classification in remote sensing satellite imagery is to use clustering in the spectral domain. The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (...

Prediction of landcover types from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to recent advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in ∼200 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. This unprecedented spectral resolution ...

APPROVED FOR PUBLIC RELEASE Land clutter issues and modelling from a perspective of L-band VV polarisation are addressed. In particular, clutter distributions in three different dimensions are discussed in detail. First a three-term model for the dependence of land clutter on grazing angle is proposed. The model is site-specific and landcover-dependent. Parameters for dominant types of landcove...

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This paper deals with the integration of domain knowledge to improve the landcover classification of a sequence of images. This new approach consists in representing the plot of land as a dynamic system and in modeling its evolution (knowledge about crop cycles, rotations and farmer practices) with the timed automata formalism. The main feature of this work is to improve the classification prov...

In present days remote sensing is most used application in many sectors. This remote sensing uses different images like multispectral, hyper spectral or ultra spectral. The remote sensing image classification is one of the significant method to classify image. In this state we classify the maximum likelihood classification with fuzzy logic. In this we experimenting fuzzy logic like spatial, spe...

It is necessary to understand land cover changes for managing and monitoring natural resources development, especially urban planning. Remote sensing geographical information systems (GIS) are proven tools assessing use changes, which helps planners advance sustainability. Google Earth Engine used in this study detect one of the rapidly growing cities Nepal. was discovered that from 2013 2019, ...

During the past 25 ka, southern Ethiopia has undergone tremendous climatic changes, from dry and relatively cold during Last Glacial Maximum (LGM, 25–18 ka) to African Humid Period (AHP, 15–5 ka), back present-day conditions. As a contribution better understand effects of climate change on vegetation lakes, we here present new Predictive Vegetation Model that is linked with Lake Balance availab...

Accurately modeled Biogenic Volatile Organic Compound (BVOC) emissions are an essential input to atmospheric chemistry simulations of ozone and particle formation. BVOC emission models rely on Basal Emission Factor (BEF) distribu5 tion maps based on emission measurements and vegetation landcover data but these critical input components of the models as well as model simulations lack validation ...