AI Augmentation to Remote Sensing Imagery in Forestry Conservation &amp; Restoration for Increased Responsive Capabilities

Gandikota, Dhanuj; Gladkova, Taissa; Tran, Kha-Ai; Bapat, Sanika; Richkus, Jenn; Arnold, Jeffrey R.

doi:10.1109/aipr57179.2022.10092215

Cited by 3 publications

(2 citation statements)

References 89 publications

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“…Let us derive the expression for multiplying the coefficient matrix by the values of the original image: 115 (6) Let us formulate the following matrix of values with low-frequency coefficients: (7) Step 5. Let us define L n filter in the form of the matrix for I [m, n, k].…”

Section: Transformation Based On the Coefficients Of The Daubechies W...mentioning

confidence: 99%

“…Close attention has been paid by researchers in [3,4] to the development of learning-based hyperspectral image compression methods, which have recently attracted much attention in the field of remote sensing. Such methods require the use of a large number of hyperspectral images during training to optimize all parameters and achieve high compression performance [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Sarinova,

Neftissov,

Rzayeva

et al. 2023

sjaitu

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This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth's surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate.

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Section: Transformation Based On the Coefficients Of The Daubechies W...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Sarinova,

Neftissov,

Rzayeva

et al. 2023

sjaitu

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Mapping Industrial Influences on Earth's Ecology

Watson,

Ellis,

Pillay

et al. 2023

Annu. Rev. Environ. Resour.

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As anthropogenic transformation of Earth's ecology accelerates, and its impacts on the sustainability of humanity and the rest of nature become more obvious, geographers and other researchers are leveraging an abundance of spatial data to map how industrialization is transforming the biosphere. This review examines the methodologies used to create such maps and how they have enhanced our understanding of how societies can abate biodiversity loss, mitigate climate change, and achieve global sustainability goals. Although there have been great advances over the past two decades in mapping industrial transformations of ecology across the planet, the field is still in its infancy. We outline future research directions to better understand anthropogenic transformation of the biosphere and the utility of integrating global maps of socioeconomic, ecological, biodiversity, and climate data to explore and inform potential pathways of human-driven social-ecological change.

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Intelligent Conservation

Dwivedi,

Mahanty,

Dwivedi

2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Artificial Intelligence (AI) emerges as a potent ally in augmenting environmental monitoring and fortifying conservation efforts. Now we have seen escalating environmental challenges and the need for sustainable conservation practices. This paper outlines the innovative applications and transformative potential of AI in managing the complexities of ecological preservation and monitoring. AI facilitates the real-time processing and interpretation of voluminous environmental data. It helps in informed decision-making and strategic planning in conservation initiatives. The employment of AI-driven models and technologies such as machine learning algorithms, computer vision and sensor networks has proven instrumental in monitoring biodiversity. AI plays a pivotal role in enabling precision conservation by facilitating the identification and prioritization of critical areas requiring immediate intervention. AI contributes to the development of smart and adaptive environmental monitoring systems capable of autonomously tracking and analysing disturbances and human encroachments.

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AI Augmentation to Remote Sensing Imagery in Forestry Conservation & Restoration for Increased Responsive Capabilities

Cited by 3 publications

References 89 publications

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Mapping Industrial Influences on Earth's Ecology

Intelligent Conservation

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