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Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

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Journal of Mountain Science 2017年第5期14卷 948-963页

作者： Michael FUCHS Adnan A.AWAN Sardar S.AKHTAR Ijaz AHMAD Simon SADIQ Asif RAZZAK Naghmah HAIDER Federal Institute for Geosciences and Natural Resources Stilleweg 230655 HanoverGermany Geological Survey of Pakistan IslamabadPakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern *** spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

关键词： Lithological mapping multispectral data Spectral library Normalized difference index Northern Pakistan

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Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite data

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Advances in Remote Sensing 2023年第4期12卷 99-122页

作者： Jephter Ondieki Giovanni Laneve Maria Marsella Collins Mito DIMA-Department of Mechanical and Aerospace Engineering Sapienza University of Rome Roma Italy SIA-School of Aerospace Engineering Sapienza University of Rome Roma Italy Department of Physics University of Nairobi Nairobi Kenya

For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R2) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R2 of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.

关键词： Soil Moisture Estimation Techniques Fusion Active Microwave multispectral data Agricultural Planning

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Deep Convolutional Feature Fusion Model for multispectral Maritime Imagery Ship Recognition

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Journal of Computer and Communications 2020年第11期8卷 23-43页

作者： Xiaohua Qiu Min Li Liqiong Zhang Rui Zhao Xi’an Research Institute of Hi-Tech Xi’an China School of Information Engineering Engineering University of PAP Xi’an China

Combining both visible and infrared object information, multispectral data is a promising source data for automatic maritime ship recognition. In this paper, in order to take advantage of deep convolutional neural network and multispectral data, we model multispectral ship recognition task into a convolutional feature fusion problem, and propose a feature fusion architecture called Hybrid Fusion. We fine-tune the VGG-16 model pre-trained on ImageNet through three channels single spectral image and four channels multispectral images, and use existing regularization techniques to avoid over-fitting problem. Hybrid Fusion as well as the other three feature fusion architectures is investigated. Each fusion architecture consists of visible image and infrared image feature extraction branches, in which the pre-trained and fine-tuned VGG-16 models are taken as feature extractor. In each fusion architecture, image features of two branches are firstly extracted from the same layer or different layers of VGG-16 model. Subsequently, the features extracted from the two branches are flattened and concatenated to produce a multispectral feature vector, which is finally fed into a classifier to achieve ship recognition task. Furthermore, based on these fusion architectures, we also evaluate recognition performance of a feature vector normalization method and three combinations of feature extractors. Experimental results on the visible and infrared ship (VAIS) dataset show that the best Hybrid Fusion achieves 89.6% mean per-class recognition accuracy on daytime paired images and 64.9% on nighttime infrared images, and outperforms the state-of-the-art method by 1.4% and 3.9%, respectively.

关键词： Deep Convolutional Neural Network Feature Fusion multispectral data Ob-ject Recognition

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