Abstract
In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA) technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM) method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD) between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods.
Highlights
Hyperspectral imagery (HSI) has found many applications in various fields, such as military, agriculture, and mineralogy [1]
Considering the similarity of deoxyribonucleic acid (DNA) strand and spectrum, can be encoded by these four code words, which is enough to describe the information of DNA by different i.e., they are both high dimensional data, DNA encoding method was utilized for hyperspectral data
multi-probe based artificial DNA encoding and matching (MADEM) can capture the rich information of spectral brightness and shape, which are both important for the
Summary
Hyperspectral imagery (HSI) has found many applications in various fields, such as military, agriculture, and mineralogy [1]. Low rank representation (Lrr) based spatial pyramid matching (SPM) method encodes the descriptors under the framework of SPM and calculates the representation in the matrix space directly [15,16] It improves the robustness of the variants of SPM. In order to overcome these shortcomings, a new multi-probe based artificial DNA encoding and matching (MADEM) method is proposed in this paper. The biggest difference between ADEM and MADEM is that the latter looks for and extracts the specific information/fragments of the encoded DNA strands, which can replace the full-encoded DNA strands for the matching and classification process in hyperspectral remote sensing imagery. MADEM aims at seeking out the temporal information of the encoded DNA strands In this method, multiple probes, defined as the extracted specific fragments of the DNA strand, is put forward for HSI.
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