English version
Gromny E. at al., (2024) Remote sensing insights into land cover dynamics and socio-economic Drivers: The case of Mtendeli refugee camp, Tanzania (2016–2022)
The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis
Milczarek, M., at al (2023). Object- Versus Pixel-Based Unsupervised Fire Burn Scar Mapping under Different Biogeographical Conditions in Europe
Wildfire detection and mapping is crucial for managing natural resources and preventingfurther environmental damage. In this study, we compared two methods of mapping burn scarsusing Sentinel-2 satellite imagery, a pixel-based approach and an object-based
Jenerowicz M., Wawrzaszek A., Drzewiecki W., Krupiński M., Aleksandrowicz S. (2019), Multifractality in Humanitarian Applications: A Case Study of Internally Displaced Persons/Refugee Camps, IEEE Journal of Selected Topics in Applied Earth Observations and Remote
The coordination of humanitarian relief is always difficult due to a lack of data required for management and planning. Remote sensing imagery can be an important source of information about the in-situ situation, notably,
Woźniak E., Aleksandrowicz S. (2019), Self-Adjusting Thresholding for Burnt Area Detection Based on Optical Images, Remote Sensing
Mapping of regional fires would make it possible to analyse their environmental, social and economic impact, as well as to develop better fire management systems. However, automatic mapping of burnt areas has proved to
Krupiński M., Wawrzaszek A., Drzewiecki W., Aleksandrowicz S., Jenerowicz M. (2019), Multifractal Parameters for Spectral Profile Description, IGARSS 2019
Hyperspectral data provides huge amount of detailed information about spectral properties of the objects. In the era of fast data volume enlargement, it is necessary to develop methods and algorithms which are capable for
Aleksandrowicz S., Wawrzaszek A., Jenerowicz M., Drzewiecki W. and Krupiński M. (2019), Local Multifractal Description of Bi-Temporal VHR Images, 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China, 2019, pp. 1-3. doi: 10.1109/Multi-Temp.2019.8866963
In this work we apply local multifractal formalism to spectral bands of bi-temporal Very High Resolution (VHR) images. Analysis of the local multifractal description and its changes between images acquired in two points in
Jenerowicz M., Wawrzaszek A., Krupiński M., Drzewiecki W., Aleksandrowicz S. (2019), Aplicability of Multifractal Features as Descriptors of the Complex Terrain Situation in IDP/Refugee Camps, IGARSS 2019
This paper presents the preliminary results of the complex terrain situation description with the utility of multifractal features. The analysis was done for two samples of Internally Displaced Person/Refugee camps (Ifo, Daadab in Kenya/Al
Gromny E., Lewiński S., Rybicki M., Malinowski R., Krupiński M., Nowakowski A., and Jenerowicz M. (2019), Creation of training dataset for Sentinel-2 land cover classification, Proc. SPIE
Supervised classification of satellite images is performed based on utilization of reference training data. Therefore, the availability and quality of reference data highly influences the results and the course of the entire classification process.
Jenerowicz M., Wawrzaszek A., Krupiński M., Aleksandrowicz S., and Drzewiecki W. (2019), Comparison of mathematical morphology with the local multifractal description applied to the image samples processing, Proc. SPIE
This paper presents the results of a preliminary comparison of two methods which are based on the mathematical approach, Mathematical Morphology and the Local Multifractal Description. Both methods are characterized by the need for
Gromny E., Lewiński S., Rybicki M., Malinowski R., Krupiński M., and Nowakowski A. (2019), Post-processing tools for land cover classification of Sentinel-2, Proc. SPIE
Post-processing is the last and often optional stage of land cover (LC) classification from satellite images. In the traditional approach, it is usually applied to remove the effect of “salt and pepper” from the