Segmentation Of High Resolution Satellite Imagery Research Articles

A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging

Image segmentation is becoming increasingly important in areas such as object-oriented image classification in the field of remote-sensing image analysis. We present a new approach for the image segmentation of a high-resolution pan-sharpened satellite image based on modified seeded-region growing and region merging. First, we conduct some pre-processing prior to image segmentation to improve segmentation quality. The initial seeds are automatically selected using the proposed block-based seed-selection method. After automatic selection of significant seeds, initial segmentation is achieved by applying the modified seeded-region growing procedure. Finally, region merging, based on a region-adjacency graph, is carried out in post-processing to obtain the final segmentation result. Experimental results demonstrate that the proposed method shows better performance than other approaches, and has good potential for its application to the segmentation of high-resolution satellite imagery.

Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions

Texture features are useful for segmentation of high-resolution satellite imagery. This paper presents an efficient feature extraction method that considers the spatial and cross-band relationships of pixels in multispectral or colour images. The texture feature of an image region is represented by the joint distribution of two texture measures calculated from the first two principal components (PCs). Similarly, the spectral feature of the region is the joint distribution of greyscale pixel values of the two PCs. The texture distributions computed by a rotation invariant form of local binary patterns (LBP) and spectral distributions are adaptively combined into coarse-to-fine segmentation based on integrated multiple features (SIMF). The feasibility and effectiveness of the SIMF segmentation approach is evaluated with multispectral high-resolution satellite imagery and colour textured mosaic images under different conditions.

Automatic Segmentation of High-resolution Satellite Imagery by Integrating Texture, Intensity, and Color Features

High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper, we develop a method and algorithmic framework for automatically segmenting imagery into different regions corresponding to various features of texture, intensity, and color. The central rationale of the method is that information from the three feature channels are adaptively estimated and integrated into a split-merge plus pixel-wise refinement framework. In the procedure for split-merge and refinement, segmentation is realized by comparing similarities between different features of sub-regions. The similarity measure is based on feature distributions. Without a priori knowledge of image content, the image can be segmented into different regions that frequently correspond to different land-use or other objects. Experimental results indicate that the method performs much better in terms of correctness and adaptation than using single feature or multiple features, but with constant weight for each feature. The method can potentially be applied within a broad range of image segmentation contexts.

A new approach for the morphological segmentation of high-resolution satellite imagery

A new segmentation method based on the morphological characteristic of connected components in images is proposed. Theoretical definitions of morphological leveling and morphological spectrum are used in the formal definition of a morphological characteristic. In multiscale segmentation, this characteristic is formalized through the derivative of the morphological profile. Multiscale segmentation is particularly well suited for complex image scenes such as aerial or fine resolution satellite images, where very thin, enveloped and/or nested regions must be retained. The proposed method performs well in the presence of both low radiometric contrast and relatively low spatial resolution. Those factors may produce a textural effect, a border effect, and ambiguity in the object/background distinction. Segmentation examples for satellite images are given.