This paper presents a novel method for feature selection by proposing an improved artificial immune recognition system (IAIRS) using the opposite sign test (OST). We use the nearest neighbor algorithm as the classifier. Forty-four data sets from the UCI and KEEL repository and from eight benchmark gene expression micro-array data sets were collected for evaluation purposes. This evaluation measures the effectiveness of the proposed approach. To investigate the capability of IAIRS, we compared our result with several features selection methods and classifier based methods. Moreover, we compared our results with the results obtained by several well-known algorithms from the previous literature. The performance measures were based on accuracy and the Cohen Kappa. A non-parametric statistical test was used to justify the performance of our proposed method. We confirmed that IAIRS is significantly better than other methods.

An improved artificial immune recognition system with the opposite sign test for feature selection

This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self- Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.

/pdf/crop-type-classification-based-on-clonal-selection-algorithm-2at4gp7af3.pdf

Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

A large number of computer vision applications rely on camera calibration. Camera self-calibration which only depends on the relationship between corresponding points of a pair of images draws much attention for its simplicity. Almost all the camera self-calibration methods rely on the solution of Kruppa equations which are difficult to be directly solved. The state-of-the-art self-calibration algorithms usually convert the solution of these equations to non-linear optimization problem, traditional optimization methods usually have the drawback of convergent to local extreme. Artificial immune system (AIS) has the ability to fast convergent to global extreme. To address this problem, we proposed an artificial immune system based method which can fast convergent to the global optimization solutions. We demonstrate the performance of the proposed method with synthetic and real data.

A new camera self-calibration method based on CSA

In this paper,some initial investigations are conducted to employ resource limited artificial immune system(RLAIS) for classification of multi-spectral remote sensing image.The proposed method trains the samples of regions of interest using the RLAIS and obtains the optimal the centers of classification.Image classification task by RLAIS is attempted and the preliminary results are provided.By the experiment,it is demonstrated that the method of this paper is superior to the three traditional algorithms,and its overall accuracy and Kappa coefficient reach 91% and 0.88 respectively.

Classification of Multi-Spectral Remote Sensing Image Based on Resource Limited Artificial Immune System

A framework of remote sensing images classification based on Artificial Immune Recognition System (AIRS) is present in this papers. The relation between key parameters and the classification results are analyzed. As shown in the experiment results, the new framework inherits robustness of AIRS relative to key parameter. Experimental results show that this framework is better than Maximum Likelihood Classification method.

Research on Computation Model and Key Parameters of AIRS Supervised Classification in Remote Sensing Images

Aiming at the problem of vehicle detection in UAV remote sensing images, adaptive anchor frame calculation is used to seek the optimal initial value anchor frame, the loss function of YOLOv5 algorithm which only uses the length width ratio of the bounding box and cannot reflect the real difference between the width and height of the bounding box and its real value, is improved. It eliminates the problem of slow convergence caused by the failure to reflect the real difference in width and height. Using this improved YOLOv5 algorithm for training and detection, the effectiveness of the algorithm for vehicle detection in UAV aerial images is verified.

Vehicle Detection in UAV Remote Sensing Image Based on Improved YOLOv5

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Research on Computation Model and Key Parameters of AIRS Supervised Classification in Remote Sensing Images

Vehicle Detection in UAV Remote Sensing Image Based on Improved YOLOv5