Segmentation and Texture Analysis (original) (raw)
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Aerial Image Segmentation: A Survey
Foundation of Computer Science FCS, New York, USA, 2017
Due to the advancement in recent times, aerial images have started gaining a widespread in every domain of science. The primary data for any region can be obtained through tables, maps, graphs, etc. but these are not sufficient enough to present a real time analysis. So, an aerial image fills in the missing element. The images obtained have to undergo a lot of processing steps to enhance their quality. One such processing is segmentation. The main goal of image segmentation is to cluster the pixels of the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In this paper, we have presented a study of various segmentation techniques applied on aerial images. The processes have been explained in detail followed by a comparative table.
Image Segmentation in Aerial Imagery: A Review
SINERGI
The problem of distinguishing objects has plagued researchers for many years because of low accuracy compared to human eyes’ capability. In the last decade, the use of Machine Learning in aerial imagery data processing has multiplied, with the technology behind it has also developed exponentially. One of those technologies is image-based object identification, which relies heavily upon data computation. To reduce the computational load, various data segmentation algorithm was developed. This study is focused on reviewing the various image segmentation technology in aerial imagery for image recognition. Literature from as far as 1981 from various journals and conferences worldwide was reviewed. This review examines specific research questions to analyze image segmentation research over time and the challenges researchers face with each method. Machine Learning has gained popularity among segmentation methods. However, Deep Learning has been aggressively put an essential role in it by...
Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information
Forum Geografi, 2015
The use of Unmanned Aerial Vehicle (UAV) to take aerial photographs is increasing in recent years. Photo data taken by UAV become one of reliable detailed-scale remote sensing data sources. The capability to obtain cloud-free images and the flexibility of time are some of the advantages of UAV photo data compared to satellite images with optical sensor. Displayed area at the data shows the objects clearly. Rural area has certain characteristics in its land cover namely ricefield. To delineate the area correctly there is an object-based image analysis methods (OBIA) that could be applied. In this study, proposed a novel method to execute the separation of objects that exist in the data with segmentation method. The result shows an effective segmentation method to separate different objects in rural areas recorded on UAV image data. The accuracy obtained is 90.47% after optimization process. This segmentation can be a valid basis to support the provision of spatial information in r...
Recent advancement in sensor technology allows very high spatial resolution alongwith multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Multitude Regional Texture Extraction for Image Segmentation of Aerial and Natural Images
IOSR Journal of Computer Engineering, 2012
Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. First for aerial and natural imaging we present region based segmentation. Homogeneous regions depend on image granularity features. Second a local threshold based multitude texture regional seed segmentation for Aerial and natural image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less natural metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries. The experimental evaluation is conducted with training samples of natural and aerial images to show the performance of multitude textural extraction for more efficient image segmentation with sharp demarcation of edge portions along with intensity levels.
Split-and-merge segmentation of aerial photographs
Computer Vision, Graphics, and Image Processing, 1988
A method of segmenting aerial photographs is described which approximates the image intensity surface by planar facets. This is accomplished using a split-and-merge approach. A combination of an F-test and a mean predicate is used to test the uniformity of regions. When two regions are merged together to form a new region, the nine variables needed to compute the least-squares plane for the new region can be computed by adding the corresponding variables for the individual regions. This leads to an efficient algorithm.
MSEG A generic region-based multi-scale image segmentation algorithm for remote sensing imagery
The objective of this research was the development of a generic image segmentation algorithm, as a low level processing part of an integrated object-oriented image analysis system. The implemented algorithm is called Mseg and can be described as a region merging procedure. The first primitive object representation is the single image pixel. Through iterative pairwise object fusions, which are made at several iterations, called passes, the final segmentation is achieved. The criterion for object merging is a homogeneity cost measure, defined as object heterogeneity, and computed based on spectral and shape features (indices) for each possible object merge. The heterogeneity is then compared to a user defined threshold, called scale parameter, in order for the decision of the merge to be determined. The processing order of the primitive objects is defined through a procedure (Starting Point Estimation), which is based on image partitions, statistical indices and dithering algorithms. Mseg provides several parameters to be defined by the end user. Mseg offers a multi-resolution algorithm which performs segmentations at several levels, and at the same time provides automatic topology of objects within each level and among levels. The algorithm was implemented in C++ and was tested on remotely sensed images of different sensors, resolutions and complexity levels. The results were satisfactory since the produced primitive objects, were compared with other segmentation algorithms and are capable of providing meaningful objects through a follow up classification step. An integration of Mseg with an expert system environment will provide an integrated object-oriented image classification system. segmentation algorithm is a better choice . The main purpose of segmentation in Object Oriented Image Analysis is not the extraction of semantic objects, but the extraction of image primitives. There are, however, applications where complex algorithms, with more specific and less generic use, can result into semantic information .
Texture-Based Segmentation of Very High Resolution Remote-Sensing Images
2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009
Segmentation of very high resolution remotesensing images cannot rely only on spectral information, quite limited here for technological reasons, but must take into account also the rich textural information available. To this end, we proposed recently the Texture Fragmentation and Reconstruction (TFR) algorithm, based on a split-and-merge paradigm, which provides a sequence of nested segmentation maps, at various scales of observation.
Our world from above: texture based segmentation for landscape analysis
2008
Abstract. Evaluation of aerial pictures is of key importance for environmental analysis given the large extensions of land to study. This paper focuses on texture based segmentation of aerial images and characterisation of landscapes. Characterisation is achieved by means of a histogram of microtexture LBP/C vectors. Segmentations is hierarchically performed in a top down way by comparing the textures of potentially similar regions by metric G.
Edge Preserving Region Growing for Aerial Color Image Segmentation
Advances in Intelligent Systems and Computing, 2014
Many image segmentation techniques are available in the literature. One of the most popular techniques is region growing. Research on region growing, however, has focused primarily on the design of feature extraction and on growing and merging criterion. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points and prone to over-segmentation. This paper presents a novel framework for avoiding anomalies like over-segmentation. In this article, we have proposed an edge preserving segmentation technique for segmenting aerial images. The approach implicates the preservation of edges prior to segmentation of images, thereby detecting even the feeble discontinuities. The proposed scheme is tested on two challenging aerial images. Its effectiveness is provided by comparing its results with those of the state-of-the-art techniques and the results are found to be better.