Infrared and visible image fusion methods and applications: A survey

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

In this paper, efforts are made to detect the areas such as vegetation, water, soil, built-up area etc. from the satellite images. Landsat 7 ETM+ satellite is used for data set of images. It gives multispectral image with low resolution and panchromatic image with high resolution. For detecting the features of the urban area we require both spatial and spectral information in image. Hence these both images are first fused using different methods. Resultant fused image is then used for classification in various areas. Decision tree algorithm is used to divide the image in various classes. Various indexes such as NDVI, NDWI, SAVI and NDBI are used as decision tree rules. Some modification is done in the NDBI formula. The final results of decision tree algorithm using original and modified NDBI are compared and it was found that the decision tree algorithm using modified NDBI gives more accurate results.

Fusion classification of multispectral and panchromatic image using improved decision tree algorithm

. Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

/pdf/integrated-shoreline-extraction-approach-with-use-of-rasat-1vba0wey48.pdf

Integrated shoreline extraction approach with use of rasat ms and sentinel-1a sar images

Monitoring coastlines is essential for scientific protection and utilization of coastal zones. Since it is very common that various types of coasts are intermixed, there is much practical significance to propose a method that has universality. Moreover, weak edges and sea noise are the main problems of coastline delineation with high-resolution multispectral imagery. To settle these problems, a new method integrating prior edge information with an edge-based active contour model (EI-ACM) is proposed in this article. First, the coarse sea region is obtained with a novel region growth method. An edge refining process is then proposed to refine the edges detected by the Canny operator and obtain the prior edge information. The prior edge information is subsequently integrated with an EL-ACM to guide its evolution. Finally, the coastline is obtained by contour evolution. To validate the proposed method, it was compared to the state-of-the-art coastline extraction method. The results proved that the proposed EI-ACM outperforms in weak edge retention and avoiding the interference of sea noise, while being robust to various type of coastal.

Coastline Extraction From High-Resolution Multispectral Images by Integrating Prior Edge Information With Active Contour Model

Coastline extraction from remote sensing images has become an indispensable means in island coastal remote sensing survey, which plays a very important role in automatic navigation, targeting and mapping work. Image segmentation and boundary extraction are important ways to obtain the position of coastline. And active contour model has become an important tool for image segmentation and boundary extraction. So in this paper we try to use active contour model to extract coastline. However excessive timeconsumption and low degree of automation are two aspects we need to resolve. Aiming to this, we use quadtreemethod to obtain an initial contour, which is close to the coastline, and then evolve to calculate the accurate result. Experiments show that the method is stable, reliable and practical.

An automatic coastline extraction method based on active contour model

The invention relates to a flowage line extracting method based on Canny edge detection and active contour model, and belongs to the technical field of image processing. According to the characteristics of GAC (Grammar Applicative and Cognitive) model and edge detection operator extracting, the Canny edge detection result is integrated into the reconfiguration boundary stop function of GAC model. Compared with the traditional GAC model, the method is more reliable, and even at the weak edge of flowage line and the severe depressed position, the ideal extracting effect can be obtained. As the method inherits the characteristic of high accuracy of Canny edge detection, the extracting accuracy is high. Due to an improved curve evolution speed, the extracting efficiency is higher than that of the traditional GAC model.

Flowage line extracting method based on Canny edge detection and active contour model

Along with the development of the modern remote sensing technology, the acquired remote sensing image data gets more and more abundant, so the primary obstruction of the application of the remote sensing technology in the future is no longer the shortage of the image resource, but the capacity that how we can get more abundant, more useful and more credible information from the image resource. Multi-sensors remote sensing image fusion is an important apart of the information acquisition of the ground observation and also an important approach to resolve the problem of the mass remote sensing image data. The processing speed becomes a key point of an algorithm if can be in general use. In this paper we designed a high-pass filtering fusion algorithm of remote sensing image data in GPU (Graphics Processing Unit) using the programmability of GPU, which is a parallel vector processor. The result shows that the algorithm runs on a GPU is much faster than the CPU-based algorithm in the case of large data. And with the volumes of fusion images data getting bigger the advantage of the velocity on GPU is more obvious then on CPU.

The High-Pass Filtering Fusion Based on GPU

The invention relates to an island or reef coastline rapid obtaining method. The method includes: firstly constructing low-resolution images for raw images, conducting Mean Shift processing, obtaining the processed low-resolution images, making tags, and obtaining the coastline by a region growing method. The Mean Shift algorithm is introduced to use color information of color images to obtain island (reef) coastline effectively and lower images of noise information such as waves in the images. Coastline obtaining speed and effect are improved due to good connectivity of ocean and pixel tags. The Mean Shift algorithm is capable of making full use of the color information and suitable for coastline obtaining of remote-sensing images.

Island or reef coastline rapid obtaining method

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Papers

An automatic coastline extraction method based on active contour model

Flowage line extracting method based on Canny edge detection and active contour model

The High-Pass Filtering Fusion Based on GPU

Island or reef coastline rapid obtaining method