Human activity has changed land covers on the earth surface significantly over decades, especially in urban areas. Mapping the urban typical land covers is critical for the analysis of environment and sustainable urban management. With the availability of various remote sensed data, urban typical land covers extraction at different scale has attracted growing attention. Linear spectral unmixing is a widely-employed technique in mapping urban land covers, which can estimate urban component abundance at sub-pixel scale, popular especially in medium spatial resolution images. However, it suffers from the endmember selection, including types and numbers of endmembers, due to the intra-class spectral variability and the inter-class spectral similarity in the feature space constructed of original spectral bands. In this paper, we propose a novel technique for mapping urban typical land covers under linear spectral unmixing model in GaoFen-5 data at 20 m resolution. A main contribution of our new technique is that we develop a new spectral analysis technique which uses the spectral trend between every two bands instead of arbitrary spectral values, demonstrated effective in spectral representation of different urban land covers. The feature space constructed based on this spectral analysis model is discriminative, which makes endmember selection effective and efficient. To validate the superiority of our new spectral analysis technique, the linear spectral unmixing was also conducted based on the original spectral bands. Experimental results illustrated that the newly spectral analysis technique show promising performance for mapping urban typical land covers in terms of RMSE and SE.
Cardiac computed tomography (CT) is widely used in clinics for diagnosing heart diseases and assessing functionality of the heart. It is therefore desirable to achieve fully automatic whole heart segmentation for the
clinical applications, since manual work can be labor-intensive and subject to bias. However, automating this
segmentation is challenging due to the large shape variability of the heart and the poor contrast between sub-
structures such as those in the right ventricle and right atrium region in CT angiography images. In this work,
we develop a fully automatic whole heart segmentation framework for CT volumes. This framework is based on
image registration and atlas propagation techniques. Also, we investigate and compare the segmentation performance using single and multiple atlas propagation and segmentation strategies. In multiple atlas segmentation,
a ranking-and-selection scheme is used to identify the best atlas(es) from an atlas pool for an unseen image. The
segmentation methods are evaluated using fifteen clinical data. The results show that the proposed multiple
atlas segmentation method can achieve a mean Dice score of 0:889±0:023 and a mean surface distance error of
1:17±1:39 mm for the automatic whole heart segmentation of seven substructures.
Cardiac functional indices, such as ejection fraction and regional wall motion/ thickening, are commonly used for
assessing the contractility and functionality of the heart in clinical practice. An important step for computer-aided
determination of functional indices is the automated segmentation of the heart from computed tomography angiography (CTA) and the partitioning of the left ventricle into 16 segments. We develop a fully automatic scheme which not only segments the whole heart from cardiac CTA images, but also partitions the left ventricle, including the blood pool and myocardium, into 16 segments of bull’s eye plot. The segmentation is based on image registration and atlas propagation techniques, whereas the bull’s eye plot is first obtained through atlas propagation and then further improved to correct inconsistency across different subjects, uneven size for each segment and “zig-zag” edges between them. In this preliminary study, a cohort of ten clinical CTA data was employed to compute and evaluate the regional functional indices as well as the global indices.
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