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This paper discusses how to take advantage of clustering techniques to analyze and extract useful information from multi-temporal images of low spatial resolution satellites to monitor the sugarcane expansion. Additionally, we introduce the SatImagExplorer system that was developed to automatically extract time series from a huge volume of remote sensing images as well as provide algorithms of clustering...
The purpose of this work is to assess the sugarcane yield variation in regional scale through NDVI images from a low resolution spatial satellite. We have used Principal Component Analysis (PCA) and Cluster Analysis to correlate sugarcane cultivated land with multitemporal NDVI images also verifying the influence of climate conditions to them. According to both techniques (PCA and clustering), clusters...
Brazil is an important sugar cane producer, which is the main resource for ethanol production, a renewable source of energy. This agricultural commodity is important to the country economy, becoming fundamental to improve models that assist the crops monitoring process. Vegetation indexes originated from remote sensing images and agrometeorological indexes can be combined to represent sugar cane fields...
This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique...
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