Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at t...

Seeing the expected technical improvements as to the spatial and spectral resolution, satellite imagery could more and more provide a basis for complex information systems for recognizing and monitoring even small-scale and short-term structural features of interests within nuclear facilities, for instance construction of buildings, plant expansion, changes of the operational status, undergroun...

With the recent advancements in image processing techniques and the increasing availability of high resolution satellite imagery, one has an increased ability to characterize human attributes. This paper will study the effects that image fusion has on the classification of Quickbird satellite imagery over a rural area in Eastern India. Two panchromatic sharpening algorithms will be used and com...

Resource stratification for ground-based and remotely-sensed methods of rangeland assessment is essential for locating appropriate sampling sites. This is particularly the case when site comparisons are required, as in the case of grazing gradient analysis, wherein the objective is to limit the number of environmental variables. This study compared stratified units derived from supervised, unsu...