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Recently, multi-source remote sensing data and their derived features such as vegetation indices, texture metrics have been frequently applied to quantitatively estimate forest above-ground biomass (AGB). However, it is still challenging to efficiently select the optimal features for modeling the forest AGB. In this study, a fast, efficient and automatic method has been proposed, called as k-nearest...
This paper proposes a deep convolutional neural networks (CNNs) based method to automatically detect suburban buildings from high resolution Google Earth imagery. Traditional methods based on low-level hand-engineered features or mid-level bag of features have great limitations in complex environment, especially in suburban areas. Inspired by the astounding achievement of CNNs in object recognition...
Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed...
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