Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
نویسندگان
چکیده
منابع مشابه
Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptua...
متن کاملSegmentation Improvement of High Resolution Remote Sensing Images based on superpixels using Edge-based SLIC algorithm (E-SLIC)
The segmentation of high resolution remote sensing images is one of the most important analyses that play a significant role in the maximal and exact extraction of information. There are different types of segmentation methods among which using superpixels is one of the most important ones. Several methods have been proposed for extracting superpixels. Among the most successful ones, we can r...
متن کاملFast and Robust Variational Optical Flow for High-Resolution Images Using SLIC Superpixels
We show how pixel-based methods can be applied to a sparse image representation resulting from a superpixel segmentation. On this sparse image representation we only estimate a single motion vector per superpixel, without working on the full-resolution image. This allows the accelerated processing of high-resolution content with existing methods. The use of superpixels in optical flow estimatio...
متن کاملImproving the Speed of Multiresolution Segmentation Using Slic Superpixels
The popular multiresolution segmentation (MRS) algorithm is time and memory consuming when dealing with large images because it uses the pixel-grid for the initial object representation. In this study, we have tested a new workflow for image segmentation of remote sensing data, starting the MRS (using the ESP2 tool) from the superpixel level (using SLIC superpixels) and aiming at dramatically r...
متن کاملImage Segmentation using SLIC Superpixels and Affinity Propagation Clustering
In this paper, we propose a new method of image segmentation, named SLICAP, which combines the simple linear iterative clustering (SLIC) method with the affinity propagation (AP) clustering algorithm. First, the SLICAP technique uses the SLIC superpixel algorithm to form an over-segmentation of an image. Then, a similarity is constructed based on the features of superpixels. Finally, the AP alg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2017
ISSN: 2072-4292
DOI: 10.3390/rs9030243