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Remote Sensing Image Segmentation using OTSU Algorithm

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TLDR
In this project, Otsu thresholding algorithm is used to segment the roads and residential areas from the vegetation areas in remote sensing images.
Abstract
In recent years, extraction of information from remote sensing images is an active topic of research. Feature extraction from an image is performed by image segmentation by dividing the image into distinct and self-seminar pixel groups. In remote sensing images, large quantity of texture information is present. So, it is difficult and time consuming process to segment objects from the background in remote sensing images. Many algorithms have been proposed for the purpose of segmentation of remote sensing images. Thresholding is a simple technique but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effectively. It is the most referenced thresholding methods, as it directly operates on the gray level histogram. In this project, Otsu thresholding algorithm is used to segment the roads and residential areas from the vegetation areas in remote sensing images.

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Large-Area Land-Cover Changes Monitoring With Time-Series Remote Sensing Images Using Transferable Deep Models

TL;DR: Wang et al. as mentioned in this paper proposed the similarity-measurement-based deep transfer learning for time-series adaptive change detection (SDTL-TSACD) model, which used a standard dynamic time warping (SDTW) distance to cluster large-scale time series into multiple subcategories with high time series similarity.
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Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

TL;DR: In this paper , the authors proposed automated segmentation of benthic habitats using unsupervised semantic algorithms, such as Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast FuzzY C-means (FFC), Otsu clustering (OS), and K-meants segmentation (KM) for underwater image segmentation.
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Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony

TL;DR: The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony, and the results of the segmentation of optical-electronic images obtained from the spacecraft are presented.
References
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Journal ArticleDOI

Survey over image thresholding techniques and quantitative performance evaluation

TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
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Polarimetric SAR Image Segmentation Using Statistical Region Merging

TL;DR: The generalized SRM (GSRM) algorithm is generalized so that it can be applied to images with larger range and multiplicative noise and can be used for single- and multi-polarized as well as for fully polarimetric SAR data.
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Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model

TL;DR: A faster, more efficient ROI detection algorithm based on an adaptive spatial subsampling visual attention model (ASS-VA) is proposed, which shows that the time spent on detection using the new algorithm is only 2-4% of that expended in the traditional visual attention models and the detection results are visually more accurate.