A combination of <i>k</i>-means clustering and entropy filtering for band selection and classification in hyperspectral images

Santos, Anderson Carlos Sousa e; Pedrini, Hélio

doi:10.1080/01431161.2016.1192700

Cited by 19 publications

(11 citation statements)

References 42 publications

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“…This was because of the utilization of cirrus and thermal bands in the algorithm, which enabled easier detection of thin and cirrus clouds using the F-mask method in comparison to the other methods. However, the F-mask method failed when the land objects were sufficiently bright, such as in the case of bare land and urban area (Figure 7f,h) [25]. The detection of small pieces of cloud is a difficult task.…”

Section: Qualitative Comparison Of Cloud Detection Results Between En-clustering and Other Similar Methodsmentioning

confidence: 99%

“…Shannon's information entropy is a criterion for measuring the amount of information, used widely in several fields of research [23,24]. Mostly, it is used as a representative of uncertainty and data fusion research in remote sensing [25]. According to the information entropy theory, since a cloudy region is more uniform compared to a cloud-free region, it implies that the cloud-free region has a much higher entropy value than that of the cloudy region.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones

Wang

Xia

et al. 2020

Remote Sensing

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Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.

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Section: Qualitative Comparison Of Cloud Detection Results Between En-clustering and Other Similar Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones

Wang

Xia

et al. 2020

Remote Sensing

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“…In [5], Nakamura et al proposed a band selection method based on optimum path forest (OPF) classifier [6] as an optimization function. In another technique, A.C. S Santos et al used k-means clustering with entropy filtering for band selection and classification in hyperspectral images [7]. They used correlation distance to cluster the similar bands, from each of which, they selected the band closest to the cluster center.…”

Section: Introductionmentioning

confidence: 99%

A Superpixel and Mutual Information Based Band Selection Method for Hyperspectral Images

Sarmah¹

2020

IJATCSE

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Dimension reduction is an important task in hyperspectral image (HSI) processing to minimize computational complexity. This work presents an efficient band selection method to select a set of highly relevant yet non-redundant bands using class-band mutual information (MI) and clustering technique.In HSI, the bands comprise continuous reflectance values, while the class labels in the ground truth are discrete. Thus before calculating class-band MI, the continuous reflectance values are converted to several discrete values using a novel graph-based superpixel generation method based on the spectral angle divergence (SAD). To ensure non-redundancy of the selected bands, clustering is performed to using correlation distance and then from each cluster only one representative band is selected using the calculated class-band MI. Experiments are carried out over three hyperspectral images to evaluate the efficacy of the proposed band selection technique. For each image, classification is performed using Support Vector Machine (SVM) classifier over both the compressed image with only the selected bands and the original image. The comparison results reveal the capability of the proposed method to maintain the classification accuracy with a substantially compact set of selected bands.

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“…In the first category, techniques transform spectral bands into a low‐dimensional feature space by compressing the HSI bands using mathematical transformation [2, 3]. In the second category, techniques choose a subset of spectral bands that are most informative [4–6]. The difference between the two approaches is substantial.…”

Section: Introductionmentioning

confidence: 99%