Local Adaptive Method Research Articles

Mapping large-scale aquaculture ponds in Jiangsu Province, China: An automatic extraction framework based on Sentinel-1 time-series imagery

Aquaculture holds significant importance in supporting food security, economic development, resource conservation, and ecological balance. However, the rapid expansion of aquaculture ponds has caused many environmental problems. Studies on the trends and spatial distribution of aquaculture ponds in Jiangsu province, China, are limited, and there is a lack of aquaculture datasets with a 10-m resolution. This study aimed to develop a novel assessment framework for mapping aquaculture ponds using the Google Earth Engine (GEE) platform and Sentinel-1 time-series images. We integrated multi-threshold segmentation and object-oriented classification to extract spatial data on aquaculture ponds from 2017 to 2021 in Jiangsu Province. Results showed that (1) local adaptive threshold methods could extract data about water bodies more effectively. Based on the time series of each image element, calculating its water frequency could effectively exclude seasonal water. (2) This method could effectively map aquaculture ponds in Jiangsu Province. The overall identification accuracy was higher than 93%, and the Kappa coefficient was higher than 0.87. These results are in accordance with those in the statistical yearbook. (3) The total aquaculture pond area was largest in 2017 (3404.00 km2) and smallest in 2021 (2939.51 km2). The aquaculture pond area in Jiangsu Province showed a slight downward trend over the five-year period. Aquaculture ponds were mainly distributed in central and southern Jiangsu and the coastal areas, with the highest density at the junction of Yangzhou, Taizhou, and Yancheng. (4) From 2017 to 2021, Aquaculture ponds spanning 104.65 km2 (accounting for 55.28% of the total reduced area) were transformed into wetlands. In the coastal zone. Our results substantiate that aquaculture pond removal can accelerate wetland restoration. This study maximised the identification of aquaculture ponds as independent targets. Taken together, these results can provide a reference for aquaculture green development and tail water management in Jiangsu Province.

UIE-FSMC: Underwater Image Enhancement Based on Few-Shot Learning and Multi-Color Space

Light propagates in water with certain attenuation, which causes quality problems, such as color cast, low contrast, and low illumination, in underwater images. Moreover, it is generally difficult to obtain a large number of real-world underwater images and the corresponding ground truth in convoluted underwater environments. To address these two problems, this article recommends an underwater image enhancement scheme based on few-shot learning and multi-color space, called UIE-FSMC. Specifically, for the first time, we propose a lightweight underwater image enhancement network based on few-shot learning. We design a new strategy to train the network. Specific training steps include the following: First, synthetic underwater images are used for large-scale pre-training, which can obtain the initial weight of the network. Then, according to the characteristics of the data, meta-learning based on supervised and unsupervised loss is suggested. It further enhances the feature expression aptitude of the network by learning the external and internal characteristics of the data. Finally, fine-tuning based on supervised and unsupervised loss is designed. It further improves the accuracy, robustness, and generalization of the network through an average strategy. In addition, we design a post-processing method founded on the RGB and LAB color spaces. In the RGB color space, we suggest a local region-based adaptive color correction method for underwater images. In the LAB color space, we design a multi-scale local adaptive contrast enhancement method for the L channel and a local region-based color balance strategy for the AB channels. Quantitative and qualitative experiments on five different underwater image datasets show that the outcomes of UIE-FSMC are superior to those of other techniques. Furthermore, application research further validates the excellent performance of UIE-FSMC.

ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation

The exploration of mutual-benefit cross-domains has shown great potential toward accurate self-supervised depth estimation. In this work, we revisit feature fusion between depth and semantic information and propose an efficient local adaptive attention method for geometric aware representation enhancement. Instead of building global connections or deforming attention across the feature space without restraint, we bound the spatial interaction within a learnable region of interest. In particular, we leverage geometric cues from semantic information to learn local adaptive bounding boxes to guide unsupervised feature aggregation. The local areas preclude most irrelevant reference points from attention space, yielding more selective feature learning and faster convergence. We naturally extend the paradigm into a multi-head and hierarchic way to enable the information distillation in different semantic levels and improve the feature discriminative ability for fine-grained depth estimation. Extensive experiments on the KITTI dataset show that our proposed method establishes a new state-of-the-art in self-supervised monocular depth estimation task, demonstrating the effectiveness of our approach over former Transformer variants.

How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison

Timely and accurately estimating the spatial distribution of CO2 emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO2 emissions, it cannot estimate the spatial distribution of different types of CO2 emissions (commercial CO2 emissions, residential CO2 emissions, light industry CO2 emissions, heavy industry CO2 emissions, and agricultural CO2 emissions). Based on the local adaptive method, this study compares the potential of POI data and NPP-VIIRS data for modeling different types of carbon emissions in China to analyze the spatial structure of carbon emissions within cities. The results showed that: (1) POI data is much more powerful and reliable than NPP-VIIRS data regarding monitoring ability at the suburbs and mountainous areas. (2) From the point of view of the estimation ability of different types of carbon emissions, in the commercial CO2 emissions and residential CO2 emissions, although the correlation coefficient between the estimation results of POI data and statistical data is not significantly improved compared with that of NPP-VIIRS data, the accuracy of the estimation results is significantly improved in terms of the spatial distribution; POI data has a significantly stronger ability to estimate industrial carbon emissions than nighttime light data. (3) From the spatial distribution structure of urban carbon emission, urban carbon emission presents a “V”-shaped distribution, with two high-value areas located in the central urban area and the industrial zone in the suburbs. This study confirms that POI data is a potential and promising data source for accurately modeling different types of carbon emissions and will help support low-carbon city management and energy allocation optimization.

Ground point cloud segmentation based on local threshold adaptive method

Ground point cloud segmentation based on local threshold adaptive method

MAPPING APPLE TREES FUTURE LAND USE POTENTIAL AS A MEANS OF CLIMATE CHANGE ADAPTATION IN EAST-MEDITERRANEAN MOUNTAINS: MOUNT-LEBANON

Agricultural zonal migrations and altitudinal shifts of high chill requirements fruit trees such as apple trees is considered a way of adaptation to climate change in mountain agriculture. This study examines near and far future options (2050-2070) of this local adaptation method in four village clusters in Mount-Lebanon, involving the expansion of agricultural lands to suitable regions under different degrees of climate change scenarios of temperature increase and precipitations regime fluctuation. A Geographic Information System (GIS) mapping calculation model was established for agricultural land evaluation which aims to locate spaces where the agriculture development indicators such as soil type, slope, future temperatures, and future precipitations will be suitable for cultivation under different climate change scenarios and models. The model does not seek the exact delineation of plots as much as the location of areas with a trend of agricultural relevance in the next 30 to 50 years. This classification is a tool to help Mount-Lebanon farmers and apple growers in adapting locally to climate change by choosing the best future spots to migrate their crops to. Results showed that most lands in which agricultural development is viable, are already in use for apple production (mainly) in the 4 clusters, leaving small parcels of land with variable agro-potentials to be developed in the future under favorable climate conditions. The agriculture potential of plots of altitude exceeding 2000 meters is to be validated in the studied area, especially since the climatic and irrigation conditions of there can present serious challenges.

Correlation coefficient local capping REMD adaptive filtering method for laser interference signal.

Considering the issue of noise reduction associated with Laser Doppler Interference (LDI) signal, the paper presented a correlation coefficient local capping robust empirical mode decomposition (REMD) filter algorithm for LDI laser sensor that enables more robust reconstruction of the displacement information from an LDI signal. The performance of the algorithm is studied, and it is shown that the algorithm is capable of removing high-frequency noise. Useful information can be extracted more easily by this method, and the Hilbert phase unwrapping displacement reconstructions method based on this algorithm has been experimentally validated. The experimental results show that the proposed method can improve the frequency separation performance in experiments, and is robust against noise interference.

A Novel Local Adaptive Percentage Split Distribution Method for Image Binarization and Classification

A Novel Local Adaptive Percentage Split Distribution Method for Image Binarization and Classification

A local adaptive discontinuous Galerkin method for convection-diffusion-reaction equations

We introduce a local adaptive discontinuous Galerkin method for convection-diffusion-reaction equations. The proposed method is based on a coarse grid and iteratively improves the solution's accuracy by solving local elliptic problems in refined subdomains. For purely diffusion problems, we already proved that this scheme converges under minimal regularity assumptions (Abdulle and Rosilho de Souza, 2019) [1]. In this paper, we provide an algorithm for the automatic identification of the local elliptic problems' subdomains employing a flux reconstruction strategy. Reliable error estimators are derived for the local adaptive method. Numerical comparisons with a classical nonlocal adaptive algorithm illustrate the efficiency of the method.

Binary Ghost Imaging Based on the Fuzzy Integral Method

The reconstruction quality of binary ghost imaging depends on the speckle binarization process. In order to obtain better binarization speckle and improve the reconstruction quality of binary ghost imaging, a local adaptive binarization method based on the fuzzy integral is proposed in this study. There are three steps in the proposed binarization process. The first step is to calculate the integral image of the speckle with the summed-area table algorithm. Secondly, the fuzzy integral image is calculated through the discrete Choquet integral. Finally, the binarization threshold of each pixel of the speckle is selected based on the calculated fuzzy integral result. The experiment results verify the feasibility of the proposed method. Compared with other methods qualitatively and quantitatively, the proposed method has high performance in reconstructing image of target.

Local Threshold Adaptive Endoscope Intestinal Center Localization Method Based on Morphological Reconstruction

Local Threshold Adaptive Endoscope Intestinal Center Localization Method Based on Morphological Reconstruction

Adaptive density-based clustering algorithm with shared KNN conflict game

Recently, density-based clustering algorithms have been garnering considerable attention in the unsupervised learning field as they can identify arbitrary cluster shapes. However, classical density-based algorithms are non-backtracking such as density-based spatial clustering of applications with noise (DBSCAN) and its improved variants. Therefore, the allocation of samples cannot be optimised during the clustering process. An incorrect division can cause subsequent errors. Furthermore, when the density is unevenly distributed, the globally uniform parameters will inevitably lead to catastrophic error consequences. To address these limitations, this study proposes an adaptive density-based clustering algorithm using the shared k-nearest neighbours (SKNN) conflict game (DC-SKCG). A local density-based adaptive cut-off distance-setting method is designed for the DC-SKCG, and a conflict game method based on SKNN is proposed to optimise the clustering process. Moreover, an automatic fusion mechanism for redundant high-density core regions is presented to (1) reduce the parameter sensitivity of the algorithm and (2) improve the parameter tolerance capacity. A series of experiments are conducted using various datasets demonstrate that DC-SKCG offers higher accuracy and robustness in most cases than state-of-the-art methods.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.

An efficient targeted ENO scheme with local adaptive dissipation for compressible flow simulation

High fidelity numerical simulation of compressible flow requires the numerical method being used to have both stable shock-capturing capability and high spectral resolution. Recently, a family of Targeted Essentially Non-Oscillatory (TENO) schemes is developed to fulfill such requirements. Although TENO has very low dissipation for smooth flow, it introduces a cutoff value CT to maintain the non-oscillatory shock-capturing property. As CT actually controls the dissipation property of TENO, the choice of CT for better shock-capturing capability also means higher dissipation for small structures. To overcome this, in this paper, a new local adaptive method is proposed for the choice of CT. By introducing a novel adaptive function based on the WENO smoothness indicators, CT is dynamically adjusted from 1.0×10−10 for lower dissipation to 1.0×10−4 for stable capturing of shock according to the smoothness of the reconstruction stencil. The numerical results of the new method are compared with those of the original TENO method and an adaptive TENO method in Fu et al. (2019) [49]. It reveals that the new method is capable of suppressing numerical oscillations near discontinuities while further improving the resolution of TENO at a low extra computational cost.

Multi‐metric domain adaptation for unsupervised transfer learning

Unsupervised domain adaptation aims to learn a classifier for the unlabelled target domain by leveraging knowledge from a labelled source domain. This study presents a novel domain adaptation framework from global and local transfer perspectives, referred to as multi-metric domain adaptation (MMDA) for unsupervised transfer learning. At the global level, MMDA minimises the marginal and within-class distances and maximises the between-class distance between domains while maintaining the features of the source domain to improve the cross-domain adaptability. At the local level, MMDA exploits both in- and cross-domain manifold structures embedded in data samples to increase the discriminative ability. The authors learn a coupled transformation that projects the source and target domain data onto respective subspace where the statistical and geometrical divergences are reduced simultaneously. They formulate global and local adaptation methods in an optimisation problem and derive an analytic solution to the objective function. Extensive experiments demonstrate that MMDA shows improvements in classification accuracy compared with several existing state-of-the-art methods.

Freshwater Resource Exploitation: New Security Challenge for Africa

Population growth and human activities such as freshwater resource exploitations continue to have shattering effects on ecological dynamics. These effects are likely not unrelated to issues emanating from climate change and global warming. There is adequate scientific evidence that supports shifting trends in the ecosystem and specifically relating to increased water use and loss of wetlands; that poses a new security challenge for Africa especially, and which without proper intervention, could be catastrophic; resulting in food shortage and profound implications on biodiversity conservation. Many measures have been put in place intended to arrest excessive water extraction; the measures include co-management, encouragement of local community adaptation methods, and good governance. It is unclear whether these methods can catch up with the speed of depletion of the water resources. There is fear that future scarcity in water resources could compound with other issues plaguing Africa to result in conflicts.

Good Spirit, Good Life: A Quality of Life Tool and Framework for Older Aboriginal Peoples.

The lack of appropriate quality of life (QoL) measures is a major barrier to planning and delivering health and aged care services for older Indigenous peoples worldwide. QoL is dependent on cultural values and priorities may vary between age groups. This project aims to develop a QoL tool for older Aboriginal Australians. The study was completed with Aboriginal Australians aged over 45 years living in Perth and Melbourne, Australia. Participatory Action Research methods were applied with an Indigenous research paradigm. Semistructured interviews were undertaken to identify the factors important to having a good life. Factors were further explored in yarning groups with older Aboriginal peoples to develop the draft QoL tool questions. Face validity of the tool was completed in two regions. The participants preferred the term "a good life" to QoL. Having a good spirit is at the core of having a good life. The protective factors for a good life were family and friends, health, culture, Elder role, respect, Country, spirituality, services and supports, community, future plans, safety and security, and basic needs. Twelve factors were identified and developed into key questions for the Good Spirit, Good Life tool. The draft tool will undergo quantitative validity testing, prior to embedding in service provision to inform care for older Aboriginal peoples. With local adaptation, the tool, accompanying framework, and participatory methods for development may have wider applicability to other Indigenous populations worldwide.

Local adaptive refinement method applied to solid mechanics

A good spatial discretization is of prime interest in the accuracy of the Finite Element Method. This paper presents a new refinement criterion dedicated to an h-type refinement method called Conforming Hierarchical Adaptive Refinement MethodS (CHARMS) and applied to solid mechanics. This method produces conformally refined meshes and deals with refinement from a basis function point of view. The proposed refinement criterion allow adaptive refinement where the mesh is still too coarse and where a strain or a stress field has a large value or a large gradient. The sensitivity of the criterion to the value or to the gradient ca be adjusted. The method and the criteria are validated through 2-D test cases. One limitation of the h-adaptive refinement method is highlighted: the discretization of boundary curves.

Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

To address problems that the loss function does not correlate well with perceptual vision in super-resolution methods based on the convolutional neural network(CNN), a novel model called the ADTV-SRGAN is designed based on the adaptive diagonal total-variation generative adversarial network. Combined with global perception and the local structure adaptive method, spatial loss based on the diagonal variation model is proposed to make the loss function can be adjusted according to the spatial features. Pixel loss and characteristic loss are in combination with the spatial loss for the fusing optimization of the total loss function such that high-frequency details of the images are maintained to improve their quality. The results of experiment show that the proposed method can obtain competitive results in objective evaluations. In subjective assessment, images reconstructed by it are clear, delicate, and natural, and it preserved edge- and texture-related details.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.