This paper proposes a method for detecting oil tank targets that combines improved blotch feature detection and SVM sample learning in order to address deficiencies in the recognition of oil tank targets in thermal infrared remote sensing images due to indistinct edge information, high noise level, and small dimensions. First of all, initial detection was conducted on the basis of blotch features and mass features. Then the features of oil tank targets and an optimal combination of classification features were generated from sample learning. Finally oil tank targets were detected through classification based on SVM sample learning. The results of the experiment show that: 1) by setting appropriate parameters, this method combines blotch features and textural features for the extraction of a more comprehensive range of TIR oil tank features that can achieve the effective detection of oil tank targets; 2) based on the detection of blotch targets, this method can filter out false targets with relatively high accuracy and is capable of more stable and efficient recognition of Type #1 and Type #2 targets.
In this paper, a method for selecting and testing the features of thermal infrared (TIR) remote sensing images of oil tank targets is proposed to address deficiencies in the recognition of TIR images due to their characteristic low contrast, striping noise, and low spatial resolution. An evaluator was used to select 22 out of 29 features including texture and geometry, and training was conducted for samples with respect to these features through the libSVM (a support vector machine). This could lead to an effective detection of quasi-circular oil tank targets with a certain degree of robustness. The one-dimensional statistical features (pixel value, mean value, variance, mid-value, and gray level histogram), LBP features, EOH features and invariant moment features are more meaningful for the recognition of TIR remote sensing images of oil tank targets; the proper selection of training samples of oil tank with background is quite important for the effectiveness of detection; difference in detection windows can also influence the effect of detection. A method for selecting and testing the features of oil tank targets in TIR remote sensing images is proposed for effective classification of such targets under certain conditions.
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