No abstract

Hyperspectral images are widely used in many applications. However, finding the appropriate hyperspectral image classification technique is a challenge. In this paper, we propose a new method by using an artificial intelligence-based method for hyperspectral image classification. The system has two parts: first, a pre-processing step, which helps the training phase to work faster; and second, the training part, which consists of calculating the neuro-fuzzy parameters. The prepared system is then applied to the classification of images. Three well-known hyperspectral datasets, including Pavia University from reflective optics system imaging spectrometer, the Botswana image from Hyperion and the Indian Pine image from airborne visible/infrared imaging spectrometer, were chosen to test the method. The final results of the experiments show that this system outperforms two classical methods of hyperspectral classification: support vector machine and spectral angle mapper. The comparison of the final results was made using two different metrics: overall accuracy and total disagreement. The proposed method increases the overall accuracy by about 5% for the Pavia University dataset, 2% for the Botswana dataset and 7% for the Indian Pine dataset. The total disagreement was reduced by about 0.01 for the Pavia University, 0.03 for the Botswana and 0.1 for the Indian Pine dataset when the proposed method was applied.

The segmentation of high resolution remote sensing images is one of the most important analyses that play a significant role in the maximal and exact extraction of information. There are different types of segmentation methods among which using superpixels is one of the most important ones. Several methods have been proposed for extracting superpixels. Among the most successful ones, we can refer to SLIC method. This method has some disadvantages among which can refer to over segmentation and noncompliance with the real objects. Here, in this study, we have tried to overcome these drawbacks and propose a novel method for segmentation of large-scale images by adding edge information to the SLIC algorithm. Three different urban data including airborn and spaceborn images with high space resolution and different objects diversity have been chosen with evaluate the proposed method. The results of the proposed method have been compared to the original SLIC algorithm and other common superpixel segmentation techniques, such as DBSCAN, and superpixel segmentation with entropy rates. The quantitative comparison of the results with the help of the standard deviation parameter within the class (WCSD) shows that in case of satellite images with an average of about 780 and 1040 units and in the case of aerial images with an average of about 220 units, the standard deviation of the produced segments in the proposed method is less than the other competing methods. The visual comparison also indicates that the components produced by the proposed method have the lowest standard deviation and are homogeneous.

<div> <p>Optimal soil nitrogen-to-phosphorus-to-potassium (NPK) stoichiometry is critical for agricultural production in Africa because it presents the appropriation of input materials to avoid limited or excessive fertilizer applications. Furthermore, an optimum nutrient supply to the plants is crucial to mitigate or eliminating Africa’s food crisis. However, what drivers influence its levels, distribution and variability across different landscapes and scales? Insights regarding these aspects are necessary for (1) the derivation of robust policies associated with crop production and food security, (2) monitoring of changes associated with hotspot areas between NPK stoichiometry and designated drivers, (3) instigation of suitable and well-targeted efforts to ensure that areas with optimal soil NPK levels are maintained since these eventually affect and influence crop yield output, and (4) identification of areas that are imbalanced in NPK content of the soils and thus may need fertilization. Freely accessible major soil nutrient data [i.e., nitrogen (N), phosphorus (P) and potassium (K)] for Africa were obtained from the iSDAsoil platform, aggregated to 250 m, and used to compute the NPK stoichiometry estimate. In addition, similar NPK stoichiometry estimates were derived for national-scale major food crop exporters, including South Africa, Ethiopia, and Malawi. All these across-scale NPK stoichiometry estimates coupled with different driver estimates (e.g., human activities/agricultural activities/cropping systems, soil texture, soil pH, etc.) provided the data used to assess pairwise mechanistic and explainable model insights using structural equation models [SEM(s)] plus partial dependence plots (PDPs) respectively. Climate-related factors along with topography were the main direct drivers of NPK stoichiometry connected to the topsoil of Africa (i.e., the entire continent including some selected nations).  Human-related activities contributed less to soil NPK stoichiometry. Interestingly, aboveground biomass was discovered to be interdependent with NPK stoichiometry. This cross-scale benchmark alludes to the variations in NPK stoichiometry under both changing climatic conditions and topography in Africa.</p> <p><strong>Keywords</strong>: NPK Stoichiometry, Soil Nutrients, Climate Change, Food Security, Structural Equation Modeling, Topographic Effects, Nitrogen, Phosphorus, Potassium, Africa</p> </div><p> </p>