Recycling scrap metal is an important way to protect the ecological environment. Design effective yet efficient techniques to automatically identify recyclable scrap metals is an important task within this topic. Due to the advantages of fast response and high accuracy, laser-induced breakdown spectroscopy (LIBS) recently played an important role in the mineral identification. However, the identification accuracy of peak-seeking is greatly affected by the data quality of the LIBS spectrum, whereas machine learning methods may be greatly affected by the number of training data. By considering the above open issues, this paper proposes a hybrid algorithm based on support vector machine (SVM) and element peak-seeking. By investing the identified difference of the major element (with the largest composition in the alloy) and the general element (with composition more than 1% in the alloy) between peak-seeking and SVM, three integration types (i.e., rejection, partial acceptance, complete acceptance) are defined. The final recognition result is generated according to different integration types and the corresponding integration methods. To verify the feasibility of the proposed approach, a simulated alloy LIBS database was established based on 31 metal elements and the simulated alloy LIBS data according to their compositions. Comparing with the result obtained by only using SVM, the proposed method greatly improved the recognition accuracy. The accuracy of identifying all general elements increased from 8% to 74.5%. Experimental results confirmed the effectiveness of the proposed method in identification of general metal elements in terms of higher detection accuracy.
The physiographic map can visualize spatial relations between different landforms, thus providing insights into geologic processes that shaped the present-day Martian landscape. The physiographic map of Mars surface is usually made through image interpretation, which is always labor-intensive and highly depends on the expert knowledge. In this paper, we propose an efficient and automatic classification method for characterization of landforms on Mars by using the Mars Orbiter Laser Altimeter (MOLA) digital elevation data. The proposed method was tested on a region where China's Mars probe Tianwen-1 landed. The study area covers the Nepenthes Mensae, Amenthes Planum, northern Terra Cimmeria, northern Hesperia Planum and southern Utopia Planitia region, having a size of 2250km×2750km centered at 117°E, 6°N. The obtained results confirm the effectiveness of the proposed method in describing different topographic characteristics of the Martian landforms. Note that the proposed method is completely data-driven, which can provide a rapid mapping result in large geographical regions, especially from a global perspective to reveal the Martian landform information.
The Mars Surface Composition Detector (MarSCoDe) is impacted by the lack of energy supply, the ambient temperature outside, and the spectral line drift issue, which makes it very difficult to analyze flight and ground data together. The BEADS algorithm is used to eliminate the spectral baseline generated by ambient light, noise, dark background, and continuous radiation signals in the original spectrum. The influence of parameters like detecting distance, focusing level, and laser energy jitter can be reduced to the fullest extent through normalization processing. To determine the mineral types and quantitative evaluation of elements on the surface of Mars as much as possible, the team tested 35 national standard minerals and obtained1750 spectra in an effort to ascertain the types of minerals and quantitative evaluation of elements as possible on the surface of Mars.. A fuzzy decision of flight data based on assa-grnn is proposed and supported by the national standard mineral database on the ground. When combined with the decision results, the wavelength of the measured Mars data is modified twice, which supports the interpretation of flight data by laboratory data. Results demonstrate that assa-grnn can flawlessly improve the wavelength transfer between the ground database and flight data of MarSCoDe.
Comparing with the multispectral remote sensing image, hyperspectral image (HSI) has higher spectral resolution, a near continuous spectral signature, thus can represent fine spectral variations that occurred in the temporal domain. This allows more spectral changes to be detected, especially major changes that reflected on the overall spectral signature (associating with the abrupt land-cover transitions), as well as subtle changes that reflect only on a portion of the spectral signature (associating with the change of physicochemical properties of the land-cover classes). Currently, there are some available hyperspectral change detection (CD) data sets. However, they have the following drawbacks. First, there is a lack of diversity in the data source; all data sets were created using the Hyperion sensor mounted on the EO-1 satellite. Second, these data sets mainly concentrate on the river and agriculture scenes, which lose their diversity for representing different land-covers. In this paper, we construct three new change detection data sets by using the multitemporal images acquired by the China’s new generation of hyperspectral satellites, i.e., OHS, GF-5 and ZY1-02D. These data sets present various event-driven land-cover changes, such as new building construction, crop replacements, and the expansion of energy facilities. Then a novel unsupervised hyperspectral change detection approach is proposed based on the intrinsic image decomposition (IID). Experimental results confirmed the effectiveness of the proposed approach in terms of higher overall accuracy by comparing with the reference techniques.
This paper proposes a novel coarse-to-fine ice-block falls detection approach based on the YOLO-V4 network and a postprocessing strategy by considering the illumination properties (i.e., adjacent distance, direction, area ratio of ice-block and shadows) of the considered ice-block targets. The proposed approach mainly consists of two steps: 1) Coarse detection of ice-block falls based on the YOLO-V4 network. 2) Extraction of the illumination properties of ice-block targets, and refine the initial detection results based on the post-processing strategy. By taking the edge of the Boreum Planum in Mars Arctic as a research region where presents frequent ice-block falls activity, the HiRISE (High Resolution Imaging Science Experiment) image was used to verify the reliability of the proposed approach. Note that in this work we only focused on the ice-block targets whose length and width are larger than 0.5m (2 pixels in the HiRISE image). Final obtained experimental results confirmed the effectiveness of the proposed approach for identifying the ice-block falls activity over large Martian areas at both local and global scales.
Hyperspectral images (HSIs) provides abundant spectral information through hundreds of bands with continuous spectral information that can be used in land cover fine change detection (CD). HSIs make it possible for hyperspectral CD performance with higher discrimination on changes but provides a challenge to the conventional CD techniques due to its high dimensionality and dense spectral representation. In this paper, we implemented intrinsic image decomposition (IID) model to decompose the hyperspectral temporal difference image into two parts: real change and pseudo change information. In particular, the spectral reflecting component is selected as a kind of pure spectral feature used to enhance the CD performance in multitemporal HSIs. Experimental results illustrate the effectiveness of IID features extraction in addressing a supervised CD task.
Different from the traditional daytime Remote Sensing (RS) observation data, Nighttime light (NTL) RS images have shown their great potential in earth observation applications from a unique point of view. With the launch of the China’s new generation Luojia1-01 (LJ1-01) NTL satellite, the acquisition of the high spatial resolution and high quality NTL imagery make it possible to identify the disaster event and its temporal change by using the automatic Change Detection (CD) techniques. It is a strong complement to the daytime remote sensing information. In this paper, we proposed a multiple feature fusion CD approach for fire disaster event monitoring in multitemporal high resolution LJ1-01 NTL images. The multiple texture features were fused by taking advantages of the Multivariate Alteration Detection (MAD) and its Iteratively-Reweighted version (IR-MAD) algorithms, in order to improve the CD performance limited by using the original single-band gray-level NTL images. Experimental results obtained on the multitemporal LJ1-01 NTL images demonstrated the effectiveness of the proposed CD technique in implementing an automatic and accurate extraction of fire disaster event of the 2018 California Camp fire. The proposed approach outperformed the ones only relying on the gray-scale original band and single texture features. The conclusion of this study explores the possibility and potential by using high resolution NTL data for CD, in particular, for the effective emergency and rescue in major disaster monitoring applications.
In this paper, we propose to apply unsupervised band selection to improve the performance of change detection in multitemporal hyperspectral images (HSI-CD). By reducing data dimensionality through finding the most distinctive and informative bands in the difference image, foreground changes may be better detected. Band selection-based dimensionality reduction (BS-DR) technique is considered to investigate in details the following sub-problems in HSI-CD including: 1) the estimated number of multi-class changes; 2) the binary CD; 3) the multiple CD; 4) the change discriminability; 5) the optimal number of selected bands. Thus it contributes at first time a quantitative analysis of the BS-DR approach impacting on the HSI-CD performance. Due to the difficulty of having training samples in an unknown environment, unsupervised band selection and change detection are considered. A pair of real multitemporal hyperspectral Hyperion data set has been used to validate the proposed approach. Experimental results confirmed the effectiveness of selecting a band subset to obtain a satisfactory CD result, comparing with the one using original full bands. In addition, the results also demonstrated that the reduced feature space is capable to maintain sufficient information for detecting the occurred spectrally significant changes. CD performance is enhanced with respect to the increasing of change representative and discriminable capabilities.
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