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Abstract. To address the issue of the information redundancy for hyperspectral remote sensing image, this paper presents a novel ensemble algorithm that merges Random Projection (RP) and Bias-corrected Fuzzy C-means (BCFCM) algorithm. Since RP matrix has the abilities of preserving information nicely, it can be used to reduce the dimension of the image. To make full advantage of neighborhood relationship, BCFCM algorithm is improved to segment the low-dimensional image, in which Euclidean distances are retained to define the similarity between hyperspectral remote sensing image and the low-dimensional image. Finally, BCFCM algorithm is used to segment the fuzzy membership matrix of the ensemble algorithm. The proposed algorithm is evaluated by real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral remote sensing images. Segmentation performance is estimated by kappa coefficient and overall accuracy. Experimental results demonstrate that the proposed algorithm can achieve higher segmentation accuracy at a lower computational cost than that from conventional algorithms.

Dimensionality reduction based on random projection (RP) includes two problems, namely, the dimensionality is limited by the data size and the class separability of the dimensionality reduction results is unstable due to the randomly generated projection matrix. These problems make the RP algorithm unsuitable for large-size Hyperspectral Image (HSI) classification. To solve these problems, this paper presents a new Partitioned RP (PRP) algorithm and proves its rationality in theory. First, a large-size HSI is evenly divided into multiple small-size sub-HSIs. Afterwards, the projection matrix that maximizes the class separability is selected from multiple samplings in which the class dissimilarity measurement is defined as large inter-class distance and small intra-class variance. By using the same projection matrix, each small-size sub-HSI is projected to generate a low dimensional sub-HSI, thereby generating a low dimensional HSI. Next, the Minimum Distance (MD) classifier is utilized to classify the low dimensional HSI obtained by the PRP algorithm. Finally, four real HSIs are used for experiments, and three of the most popular classification algorithms based on RP are selected as comparison algorithms to validate the effectiveness of the proposed algorithm. The classification performance is evaluated with the kappa coefficient, overall accuracy (OA), average accuracy (AA), average precision rate (APR), and running time. Experimental results indicate that the proposed algorithm can obtain reliable classification results in a very short time.

The problem that the randomly generated random projection matrix will lead to unstable classification results is addressed in this paper. To this end, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. In particular, this paper presents a random projection matrix selection strategy based on the separable information of a single class able to project the features of a certain class of objects. The projection result is measured by the degree of separability, thereby obtaining the low-dimensional image with optimal separability of the class. After projecting samples with the same random projection matrix, to calculate the distance matrix, the Minimum Distance classifier is devised, repeating for all classes. Finally, the weight of the distance matrix is considered in ensemble classification by using the information entropy. The proposed algorithm is tested on real hyperspectral remote sensing images. The experiments show an increase in both stability and performance.