Split-and-merge segmentation of aerial photographs (original) (raw)

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.

A robust method for picture segmentation based on a split-and-merge procedure

Computer Vision, Graphics, and Image Processing, 1986

The aim of picture segmentation is the extraction of pertinent and stable areas. Pertinence is the agreement of the detected areas with a physical or semantical property of the object; stability is the robustness of the detection to slight transformations such as geometric or photometric distortions. In aerial picture segmentation, the pertinence of an area is often reduced to radiometric homogeneity and spatial connectivity. Unfortunately stability is seldom checked and the deduced segmentation is very sensitive to many parameters introduced by the programmer and thus it is not very reliable. We propose a solution to the stability problem. It will be presented in a theoretical way and then an example of an application is proposed. This method makes use of the well-known split-and-merge algorithm and we will first recall its principle and its main properties.

Segmentation and Texture Analysis

This paper describes the state of the art in segmentation algorithms of aerial images. Different approaches and object classes are described and their advantages and limitations are shown. First the advantage of multiple input data (e.g., color, infrared, DEM) and the information that can be derived from these sources is discussed. Besides sensor data, “synthetic” input images (e.g., using texture filters) are generated to support the segmentation process. After an optional noise cleaning, primitives are extracted in scale space. This offers the possibility of selecting an optimal resolution depending on the size and shape of an object. Using this resolution, the raw segmentation will be stable and conflicts with other object classes will be reduced. Depending on the class of the object the final extraction has to be selected: Compact artificial objects can be segmented using primitives like areas, lines, or points. Linear objects like roads are similar but the borders are curves and the size is not limited. Arbitrary areas like meadows, forests, or fields have an arbitrary border and are mainly defined by their specific texture. Objects like trees or cars have to be treated in a very specific manner. Finally, different base algorithms for segmentation are discussed: Pixel classification is very simple but lacks the use of context. The extraction of primitives (egdes, lines, area, points) can be used as a basis for a wide class of objects. Texture analysis can be used for a rough segmentation of the image. Specialized operations are useful for the extraction of objects like single trees or to support the interpretation process.

SEGMENTATION OPTIMIZATION FOR AERIAL IMAGES WITH SPATIAL CONSTRAINTS

Unsupervised segmentation methods are important to extract boundary features from large forest vegetation databases. Finding optimized segmentation algorithms for images with natural vegetation is crucial because of the computational load and the required reproducibility of results. In this paper, we present an approach how to automatically select optimized parameter values for JSEG segmentation. The parameter evaluation is based on a spatial comparison between segmented regions and manually acquired ground truth. City block distance will be used as error metric to define discrepancies between available ground truth and segmentation. Varying the parameter range of values systematically allows to compute corresponding error areas. The smallest error area represents the optimized parameter value.Dependent on the lightness distribution of the selected images and the chosen color quantization, the spatial comparison with the ground truth is limited to local optimization.

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...

Image segmentation using a generic, fast and non-parametric approach

1998

In this paper, we investigate image segmentation by region merging. Given any similarity measure between regions, satisfying some weak constraints, we give a general predicate for answering if two regions are to be merged or not during the segmentation process. Our predicate is generic and has six properties. The first one is its independance with respect to the similarity measure, that leads to a user-independant and adaptative predicate. Second, it is non-parametric, and does not rely on any assumption concerning the image. Third, due to its weak constraints, knowledge may be included in the predicate to fit better to the user's behaviour. Fourth, provided the similarity is well-chosen by the user, we are able to upperbound one type of error made during the image segmentation. Fifth, it does not rely on a particular segmentation algorithm and can be used with almost all region-merging algorithms in various application domains. Sixth, it is calculated quickly, and can lead with appropriated algorithms to very efficient segmentation.

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.

An Approach for Semi-Automatic Extraction of Features from Aerial Photographs

Aerial photographs have been evaluated manually by the operators for the extraction of the vector data to produce photogrammetric maps. In the recent years the developments, in the photogrammetry, provides to perform these extraction processes automatically. In this study, a new semi-automatic feature extraction approach, based on the segmentation of the images using color-differences of the pixels and the propogation of fronts by the Level Set algorithms, is developed. An object-oriented application software is also developed to test the capabilities of the developed method. Some semi-automatic feature extraction applications are made by the help of the developed software using 1:4000 and 1:35000 scale black/white aerial photographs for determining the capabilities of this method. The results of the tests show that this method can be used for the extraction of some features from aerial photographs for GIS and the production of the photogrammetric maps.

Automatic Image Segmentation Optimized by Bilateral Filtering

2010

The object-based methodology is one of the most commonly used strategies for processing high spatial resolution images. A prerequisite to object-based image analysis is image segmentation, which is normally defined as the subdivision of an image into separated regions. This study proposes a new image segmentation methodology based on a self-calibrating multi-band region growing approach. Two multispectral aerial images were used in this study. The unsupervised image segmentation approach begins with a first step based on a bidirectional filtering, in order to eliminate noise, smooth the initial image and preserve edges. The results are compared with ones obtained from Definiens Developper software.

Semi-Automatic Extraction of Features from Digital Imagery

2000

Aerial photographs have been evaluated manually by the operators for a long time for the extraction of the vector data. The development of computer technology and digital image processing technologies provide to perform these extraction processes automatically or semi-automatically. Image segmentation can be used for automatic and semi-automatic feature extraction and classification of images. In recent years, image segmentation and