Abstract
Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction of a spectrally homogeneous and spatially contiguous region from a user selected seed pixel. The proposed technique combines divisive clustering, connected component analysis, and image noise estimation to generate a series of candidate regions of increasingly smaller size until they converge to the seed pixel through similarity space. From these candidate regions, an optimal one is identified that is spectrally homogeneous, spatially contiguous, and as large as possible. Experimental results demonstrate that the proposed method achieved detection rates of up to 95%, false alarm rates below 1%, and was robust to the main user input, the seed pixel location.
Highlights
Seed region growing (SRG) techniques are simple, fast, and effective image segmentation algorithms.[1,2] These methods require the selection of seed pixels that satisfy some user criterion grow the seeds into segments by absorbing adjacent pixels until a statistical, visual, or physical criterion is met
An automated extraction method of spectrally homogeneous region of interest (ROI) has been developed for remotely sensed images
The ROI is generated based on an in-scene estimation of the image noise level
Summary
Seed region growing (SRG) techniques are simple, fast, and effective image segmentation algorithms.[1,2] These methods require the selection of seed pixels that satisfy some user criterion grow the seeds into segments by absorbing adjacent pixels until a statistical, visual, or physical criterion is met. The goal of these algorithms frequently is segmenting the entire image[3] or extracting a domain specific region of interest (ROI) such as vegetation,[4] cancer masses,[5] or road networks.[6] Another popular technique, superpixels,[7] over-segments an image into patches of similar pixels that help define visually meaningful boundaries This method requires a priori knowledge, a database of exemplars, and a classifier trained using features based on the classical gestalt theory of grouping. These approaches introduce additional requirements not needed when the goal is a simpler problem, extracting spectrally homogeneous ROIs which need not correspond to an object’s visual boundaries
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