Abstract
The fine classification of crops is critical for food security and agricultural management. There are many different species of crops, some of which have similar spectral curves. As a result, the precise classification of crops is a difficult task. Although the classification methods that incorporate spatial information can reduce the noise and improve the classification accuracy, to a certain extent, the problem is far from solved. Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery is presented. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to reduce the spectral variation within the homogenous regions and accurately identify the crops. The experiments on hyperspectral datasets of the cities of Hanchuan and Honghu in China showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information. This method has important significance for the fine classification of crops in hyperspectral remote sensing imagery.
Highlights
The accurate identification of crop types is an important basis of agricultural monitoring, crop yield estimation, growth analysis, and determination of crop area and spatial distribution [1,2]
In this paper, we propose the method of spectral–spatial fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in hyperspectral imagery, which is designed to fuse the spatial and spectral features of the high spatial resolution hyperspectral data by combining suitable potential functions in a pairwise conditional random field model
After MNF transformation, the components are arranMgeindimacucmordninoigsetofrthacetisoignn(aMl-tNo-Fn)oriosetartaiotino,iws ahecroemthmeoinnfloyrumsaetdiomn eisthmoadinfloyr ceoxntrcaecnttirnagtesdpiencttrhael ffierasttucroems, paonndeintti.sAbsotthhesicmomplpeoannedntesaisnycrtoeaisme,ptlheemiemnat.gAe fqteuralMityNgFrtardaunasflolyrmdeactiroena,seths.eSctoumdipeosnheanvtes ashreowarnratnhgaet,dcoamccpoardreindgwtiothththeesiogrnigailn-taol-nhoigishe-driamtieon, swiohnearleitmheaginefdoartmaaatniodnthisemfeaaitnulrye cimonacgeenotrbattaeidneind by principal component analysis (PCA) transformation, the low-dimensional feature image obtained by MNF transformation can extract the spectral information more effectively [44]
Summary
The accurate identification of crop types is an important basis of agricultural monitoring, crop yield estimation, growth analysis, and determination of crop area and spatial distribution [1,2]. In this paper, we propose the method of spectral–spatial fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in hyperspectral imagery, which is designed to fuse the spatial and spectral features of the high spatial resolution hyperspectral data by combining suitable potential functions in a pairwise conditional random field model In this method, to reduce the spectral changes within homogenous regions, preserve details, and alleviate the problem of excessive smoothing, SSF-CRF selects representative features from the perspectives of mathematical morphology, spatial texture, and mixed pixel decomposition to form the spatial feature vector, and combines them with the spectral information of each pixel to form the spectral–spatial fusion feature vector. It thereby maintains the integrity of the homogeneous regions and the shape structure of the features by simulating the spatial contextual information of each pixel and its corresponding field through the label field and the observation field
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