12,879 research outputs found
Spatial prioritisation of revegetation sites for dryland salinity management: an analytical framework using GIS
[Abstract]: To address the lack of analytical and modelling techniques in prioritising revegetation sites for dryland salinity management, a case study of the Hodgson Creek catchment in Queensland, Australia, was conducted. An analytical framework was developed, incorporating the use of spatial datasets (Landsat 7 image, DEM, soil map, and salinity map) which were processed using image processing techniques and a geographic information system (GIS). Revegetation sites were mapped and their priority determined based on recharge area, land use/cover and sub-catchment salinity. The analytical framework presented here enhances the systematic use of land information, widens the scope for scenario testing, and improves the testing of alternative revegetation options. The spatial patterns of revegetation sites could provide an additional set of information relevant in the design of revegetation strategies
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images
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