Objective

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Marine resource survey is an important component of marine scientific research

which is of great significance to the development and utilization of marine resources and the protection of the marine environment. Polymetallic nodules

otherwise known as manganese nodules

are commonly distributed in the submarine plains of the water depth of 4~6 km. These nodules contain metallic elements

such as manganese

nickel

copper

and cobalt. Polymetallic nodules are regarded as an important oceanic mineral resource. The rich metallic elements of these nodules are relatively rare in terrestrial resources

but they are widely used. Influential countries and research institutions are currently conducting forward-looking investigations on the potential of deep-sea manganese nodules in view of its crucial strategic value. Traditional methods for investigating manganese nodules on the ocean floor are based on mechanical sampling

multi-beam acoustic remote sensing

and optical images. In recent years

the application of hyperspectral imaging technology in deep-sea resource surveys has gradually attracted the attention of researchers. Hyperspectral image refers to the integration of hundreds of continuous spectral bands and high-resolution image data. Each pixel corresponds to a continuous spectral curve

and objects of different materials obtain their unique "optical fingerprints". Using deep-sea hyperspectral images to investigate manganese nodule resources on the ocean floor has more comprehensive information and accurate identification than traditional image methods. This situation is precisely because many prospecting methods based on hyperspectral images have been proposed and applied to the investigation of ocean floor manganese nodules

such as the active hyperspectral imager mounted underwater

due to the superiority of these images in spectral characteristics compared with ordinary images. Platforms or remotely operated submersibles conduct surveys of seabed resources. At present

most processing methods for underwater hyperspectral images use supervised learning methods. However

supervised classification requires the addition of prior data. The following two problems occur in the investigation of marine resources: first

samples are sometimes difficult to obtain

and a perfect spectral database of seabed resources is lacking; second

underwater data are often complicated

and the result of manual calibration largely depends on the experience of the calibration personnel. This condition is vulnerable to interference from certain factors

such as fatigue

chromatic aberration

and environment

and the result will be a larger error. To address this limitation

this work proposes a deep kernel fuzzy C-means (DKFCM) clustering algorithm that combines deep network and fuzzy kernel clustering to realize unsupervised clustering of hyperspectral images of ocean floor manganese nodules.

Method

2

To extract more effective hyperspectral deep features

the DKFCM algorithm adds a random deep convolutional network Rdnet before improving the fuzzy kernel clustering algorithm to extract the deep spectral information about deep-sea hyperspectral images and use them for hyperspectral image clustering. In the feature extraction network part

the information redundancy is large because the hyperspectral image is composed of a substantial amount of relevant narrow-band spectral information. The huge spectral information can be compressed

the calculation speed is raised

and the calculation cost of the convolution operation is reduced through the dimensionality reduction operation with the principal component analysis(PCA). Then

data after PCA are convolved by randomly selecting some pixels from the data and cutting out a square image around it as the convolution kernel. After the random block convolution operation

the ReLU nonlinear activation function is added to improve the overall feature sparsity. Finally

the first layer of features is obtained. The above-mentioned dimensionality reduction

convolution

and nonlinear activation operations are repeated to extract deeper features. The shallow and deep features are merged as input features for subsequent classification. The clustering algorithm easily becomes unstable and fall into a local optimum because the initial clustering center of the KFCM algorithm is randomly selected. To improve this problem

the improved KFCM algorithm proposed in this work uses the Euclidean distance to measure the similarity and sets the distance between the initial cluster centers as far as possible. The initial cluster centers are finally iterated depending on this rule

and the clustering operation is performed. Finally

the accuracy of manganese nodules was obtained by separating and comparing each kind of substance.

Result

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The experiment is divided into three groups for comparison. The clustering algorithms used are K-means clustering

DKmeans (K-means clustering through Rdnet)

and the DKFCM clustering proposed in this work. Results show that DKFCM unsupervised clustering can effectively cluster ocean floor resources. In combination with the image data

the accuracy rate of manganese nodules is 76.59%. The overall number of correct nodules increases

the number of false clustering is relatively minimal

and the cluster results are the best. The accuracy of this algorithm is increased by 20.99% compared with K-means clustering. Meanwhile

the accuracy of the algorithm is increased by 13.76% compared with DKmeans clustering. This finding proves that the DKFCM algorithm can achieve high accuracy even without labeled data.

Conclusion

2

The hyperspectral deep fuzzy kernel clustering method proposed in the thesis

which combines deep convolutional network and improved kernel fuzzy clustering method

can extract deep features from deep sea hyperspectral data and use it for clustering. Experimental results show that the DKFCM algorithm realizes unsupervised clustering of deep-sea manganese nodules

which can be utilized for the assessment of marine resources

in the absence of seabed tag data.