IEEE Trans Image Process - Saliency and gist features for target detection in satellite images.

Tópicos

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Resumo

Reliably detecting objects in broad-area overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large intraclass variability, e.g., finding "boats" or "buildings," where model-based approaches tend to fail because no good model or template can be defined for the highly variable targets. This paper explores an automatic approach to detect and classify targets in high-resolution broad-area satellite images, which relies on detecting statistical signatures of targets, in terms of a set of biologically-inspired low-level visual features. Broad-area images are cut into small image chips, analyzed in two complementary ways: "attention/saliency" analysis exploits local features and their interactions across space, while "gist" analysis focuses on global nonspatial features and their statistics. Both feature sets are used to classify each chip as containing target(s) or not, using a support vector machine. Four experiments were performed to find "boats" (Experiments 1 and 2), "buildings" (Experiment 3) and "airplanes" (Experiment 4). In experiment 1, 14 416 image chips were randomly divided into training (300 boat, 300 nonboat) and test sets (13 816), and classification was performed on the test set (ROC area: 0.977 ? 0.003). In experiment 2, classification was performed on another test set of 11 385 chips from another broad-area image, keeping the same training set as in experiment 1 (ROC area: 0.952 ? 0.006). In experiment 3, 600 training chips (300 for each type) were randomly selected from 108 885 chips, and classification was conducted (ROC area: 0.922 ? 0.005). In experiment 4, 20 training chips (10 for each type) were randomly selected to classify the remaining 2581 chips (ROC area: 0.976 ? 0.003). The proposed algorithm outperformed the state-of-the-art SIFT, HMAX, and hidden-scale salient structure methods, and previous gist-only features in all four experiments. This study shows that the proposed target search method can reliably and effectively detect highly variable target objects in large image datasets.

Resumo Limpo

reliabl detect object broadarea overhead satellit imag becom increas press need capabl imag acquisit grow rapid problem particular difficult presenc larg intraclass variabl eg find boat build modelbas approach tend fail good model templat can defin high variabl target paper explor automat approach detect classifi target highresolut broadarea satellit imag reli detect statist signatur target term set biologicallyinspir lowlevel visual featur broadarea imag cut small imag chip analyz two complementari way attentionsali analysi exploit local featur interact across space gist analysi focus global nonspati featur statist featur set use classifi chip contain target use support vector machin four experi perform find boat experi build experi airplan experi experi imag chip random divid train boat nonboat test set classif perform test set roc area experi classif perform anoth test set chip anoth broadarea imag keep train set experi roc area experi train chip type random select chip classif conduct roc area experi train chip type random select classifi remain chip roc area propos algorithm outperform stateoftheart sift hmax hiddenscal salient structur method previous giston featur four experi studi show propos target search method can reliabl effect detect high variabl target object larg imag dataset

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