Representative Band Selection for Hyperspectral Image Classification

Xie, Fuding; Li, Fangfei; Lei, Cunkuan; Liu, Ke

doi:10.3390/ijgi7090338

Cited by 56 publications

(17 citation statements)

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“…In addition to data acquisition issues, selection of proper classification methodology is important in order to generate reliable crop classification results. Since the 2000s, machine learning algorithms such as random forest (RF) and support vector machine (SVM) were widely applied to crop classification with remote sensing data [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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Section: Introductionmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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“…In 2017, Cao et al [19] improved a classification map algorithm for fast hyperspectral selection; Shah et al [20] proposed an algorithm of the dynamic frequency domain to realize band selection. In 2018, Wang et al [21] proposed the optimal clustering framework to achieve hyperspectral band selection; Xie et al [22] made modeling and analysis according to the representativeness of the bands. In 2019, Sun et al [23] used a weighted kernel regulation to realize band selection.…”

Section: Introductionmentioning

confidence: 99%

Material Discrimination Algorithm Based on Hyperspectral Image

Zhou¹,

Wang

Jiao³

et al. 2021

Scientific Programming

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Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way.

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“…Therefore, adding new features to the classification algorithm might reduce the accuracy instead of improving it. Other than that, employing all the features available means a higher computational cost, such as happens with the use of spectral, spatial and textural object image attributes, and methods to reduce the feature space dimensionality have been studied by several authors (Guo et al, 2006;Gasca et al 2006;Zhang and Chau, 2009;Bartenhagen et al 2010;Geng et al, 2014;Xie et al, 2018Habermann et al, 2018. Also, decreasing the number of descriptors in the feature space can also reduce the computational cost, for it requires less storage capacity.…”

Section: Introductionmentioning

confidence: 99%