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Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is "from scientific rice to world rice", is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development. [ABSTRACT FROM AUTHOR]

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam's forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies. [ABSTRACT FROM AUTHOR]

Coastal wetlands are important areas with valuable natural resources and diverse biodiversity. Due to the influence of both natural factors and human activities, the landscape of coastal wetlands undergoes significant changes. It is crucial to systematically monitor and analyze the dynamic changes in coastal wetland cover over a long-term time series. In this paper, a long-term time series coastal wetland remote sensing classification process was proposed, which integrated feature selection and sample migration. Utilizing Google Earth Engine (GEE) and Landsat TM/ETM/OLI remote sensing image data, the selected feature set is combined with the sample migration method to generate the training sample set for each target year. The Simple Non-Iterative Clustering-Random Forest (SNIC-RF) model was ultimately employed to accurately map wetland classes in the Liaohe Estuary from 1985 to 2023 and quantitatively evaluate the spatio-temporal pattern change characteristics of wetlands in the study area. The findings indicate that: (1) After feature selection, the accuracy of the model reached 0.88, and the separation of the selected feature set was good. (2) After sample migration, the overall accuracy of sample classification in the target year ranged from 87 to 94%, along with Kappa coefficients of 0.84 to 0.92, thereby ensuring the validity of classification sample migration. (3) SNIC-RF classification results showed better performance of wetland landscape. Compared with RF classification, the overall classification accuracy was increased by 0.69-5.82%, and the Kappa coefficient was increased by 0.0087-0.0751. (4) From 1985 to 2023, there has been a predominant trend of natural wetlands being converted into artificial wetlands. In recent years, this transition has occurred more gently. Finally, this study offers valuable insights into understanding changes and trends in the surface ecological environment of the Liaohe Estuary. The research method can be extended to other types of wetland classification and the comprehensive application of coastal wetland in hydrology, ecology, meteorology, soil, and environment can be further explored on the basis of this research, laying strong groundwork for shaping policies on ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Remote Sensing & GIS / Türk Uzaktan Algılama ve CBS Dergisi is the property of Halil Akinci and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Change detection in Land Use and Land Cover (LULC) on remote sensing images is essential for urban planning, disaster risk management, climate change monitoring, and biodiversity conservation. Precise detection of these changes is heavily impacted by the classification accuracy of the LULC types which can be improved significantly by addressing the misclassification errors arising due to similar spectral LULC types and overlapping LULC regions. This paper proposes a texture-aware and seed-demanding Enhanced Simple Non-Iterative Clustering (ESNIC) segmentation algorithm and Boundary-Specific Two-Level (BSTL) classification approach that reduces misclassification rates due to similar spectral signatures and minimizes computational redundancy. Incorporating texture features extracted through the Gray-Level Co-occurrence Matrix along with spectral information in the proposed ESNIC segmentation algorithm improves the ability to distinguish between different LULC types that share the same spectral value. The seed demanding ESNIC segmentation approach seeds are strategically placed based on the content adaptation approach rather than being uniformly distributed throughout the image which reduces segmentation time, providing a substantial advantage for large-scale land cover mapping. A BSTL classification approach that synergistically combines the Support Vector Machine’s ability to effectively handle high dimensional data with the k-Nearest Neighbor’s ability to handle irregular data is used. This study is assessed in terms of Overall Accuracy(OA), Producer Accuracy, User Accuracy, kappa coefficients (K), Root Mean Square Errors (RMSE), and F1 scores. Results indicate that the proposed ESNIC-BSTL (OA = 97.18%, $\text {K} = 0.96$ and RMSE =0.1311) approach provides better accuracy than SNIC-SVM (94.42%, 0.92, and 0.1422) and SNIC- BSTL (95.78%, 0.94 and 0.1362).

Objectives: Land cover classification in national parks plays an important role in understanding the status of natural resources, identifying the existing ecological security threats and responding to them quickly. Methods: Two land cover classification methods are developed based on Google Earth Engine (GEE) platform by combining Sentinel active and passive remote sensing data, and spectral indices, textural features and topographic features derived from the data to classify land cover types in the Qianjiangyuan National Park (cropland, forest, grassland, water body, artificial surface and bare land). One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm. Results: The ground experimental results show that the highest overall classification accuracies of the pixel-based method and the object-oriented method are 92.37% and 93.98%, respectively. Furthermore, the integration of synthetic aperture radar (SAR) data can substantially improve the classification accuracy when using the pixel-based method, but there is no apparent escalating effect for the object-oriented method. Conclusions: Land cover classification map generated by SNIC+RF algorithm in GEE platform is more complete and the algorithm requires fewer features and runs quickly in GEE platform. Thus, this algorithm deserves to be popularized in national park management practices. [ABSTRACT FROM AUTHOR]

Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we used the Google Earth Engine (GEE) and Sentinel-1A/B SAR time series to develop eight types of crop classification strategies based on different sampling methods of central and scattered, different perspectives of object-based and pixel-based, and different classifiers of the Time-Weighted Dynamic Time Warping (TWDTW) and Random Forest (RF). We carried out 30-times classifications with different samples for each strategy to classify the crop types at the North Dakota–Minnesota border in the U.S. We then compared their classification accuracies and assessed the accuracy sensitivity to sample size. The results found that the TWDTW generally performed better than RF, especially for small-sample classification. Object-based classifications had higher accuracies than pixel-based classifications, and the object-based TWDTW had the highest accuracy. RF performed better in scattered sampling than the central sampling strategy. TWDTW performed better than RF in distinguishing soybean and dry bean with similar curves. The accuracies improved for all eight classification strategies with increasing sample size, and TWDTW was more robust, while RF was more sensitive to sample size change. RF required many more samples than TWDTW to achieve satisfactory accuracy, and it performed better than TWDTW when the sample size exceeded 50. The accuracy comparisons indicated that the TWDTW has stronger temporal and spatial generalization capabilities and has high potential applications for early, historical, and limited-samples-based crop type classification. The findings of our research are worthwhile contributions to the methodology and practices of crop type classification as well as sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Mapping informal settlements' diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity. [ABSTRACT FROM AUTHOR]

Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.

Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

The planting distribution of paddy rice has been widely extracted to identify the flooding characteristics of paddy rice during the transplanting period. Optical remote sensing images can be mostly used as the data source at present. However, the transplanting period cannot characterize full spectral characteristics of the paddy rice lifespanthe, spectral characteristics of paddy rice should be considered in multiple phenological periods. The pixel-based extraction of paddy rice planting distribution can also be susceptible to the data source noise. Fortunately, an object-oriented extraction can be selected to effectively reduce the impact of data source noise in the field. Since the remote sensing images are limited by the acquisition and processing costs, only a few studies focused on the application of multiple paddy rice phenological stages in the extraction of paddy rice planting distribution in large areas. Alternatively, the emergence of the Google Earth Engine (GEE) platform in earth science data and analysis can be accessible to large amounts of remote sensing data for free and with high efficiency. Taking Panjin City, Liaoning Province of China as the research area, this study aims to realize the object-oriented extraction of rice planting areas using phenological features from the GEE platform. Four phenological periods of paddy rice were selected, namely the sowing, transplanting, heading, and maturity period. Specifically, the sowing period with the Bare Soil Index (BSI) was selected from March 15 to April 30. The transplanting period was selected as the Green Chlorophyll Vegetation Index (GCVI) and the Modified normalized difference water index (MNDWI) from May 10 to June 20. The heading period was the Normalized Difference Red Edge Index (NDRE) and Normalized difference vegetation index (NDVI) from June 30 to September 10. The maturity period with the Plant Senescence Reflectance Index (PSRI) was selected from September 20 to October 20. The 2020 Sentinel-2 time series images were filtered to construct the datasets in the four paddy rice phenological periods. Then, the spectral indices corresponding to each phenological period were calculated and synthesized by the median. Finally, the six images were synthesized into one multi-band image as the original image. The images were segmented with the Simple Non-Iterative Clustering (SNIC) available in the GEE platform. Texture features were also calculated with the Gray Level Co-occurrence Matrix (GLCM). Six models were also established for the paddy rice distribution using the random forest (RF) and Support Vector Machine (SVM), including the pixel-based RF model (PB_RF), object-oriented RF model (SNIC_RF), object-oriented RF model with texture features (SNIC_GLCM_RF), pixel-based support SVM model (PB_SVM), object-oriented SVM model (SNIC_SVM), and object-oriented SVM model with texture features (SNIC_GLCM_SVM). A full extraction of paddy rice planting distribution was implemented using the six models in the study area in 2020. An optimal model was achieved to verify the model accuracy in the field survey of paddy rice. The results show that the RF performed better than the SVM in the extraction of paddy rice planting distribution. The object-oriented method can be widely expected to improve the extraction accuracy of paddy rice planting distribution. The highest extraction accuracy was also achieved in the SNIC_RF model. Correspondingly, the overall accuracy and Kappa coefficient were 96.83% and 0.934, respectively. The field survey data was used to verify the model, where the field survey accuracy of paddy rice was 95.43%, fully meeting the requirements of regional paddy rice planting distribution and area monitoring. [ABSTRACT FROM AUTHOR]

Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy. [ABSTRACT FROM AUTHOR]

Mapping informal settlements’ diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research. [ABSTRACT FROM AUTHOR]

We propose an unsupervised segmentation method based on simple non-iterative clustering (SNIC) and adaptive density-based spatial clustering of applications with noise (DBSCAN). The method is not sensitive to parameter settings. And cluster parameter suitable for each image can be automatically calculated. SNIC superpixel segmentation is applied in achieving over-segmented images to solve the problem of the image resolution being too high. Then, adaptive DBSCAN clustering is proposed to cluster the over-segmented superpixel blocks to solve the problem of over-segmentation and manual adjustment of DBSCAN parameters. Finally, k-means and connected regions are used for postprocessing to remove the shadow superpixel blocks from the clustered image and to ensure the integrity of a single microstructure. The effectiveness of this method is proved by many experiments. Based on this method, we provide a fast labeling method to help experts quickly label metallographic images. [ABSTRACT FROM AUTHOR]