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Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]

As technology develops, the use of artificial intelligence is increasingly widespread, including in remote sensing. Artificial intelligence can also be used to obtain various information from satellite imagery such as image processing, recognition, or identification. The existence of this technology is in-line with the increasing availability of various existing satellite images. Artificial intelligence can assist users in detecting objects in the image more faster than the traditional ones. In the identification process through image, training data is needed in the form of a dataset with a certain label. In this paper, we will discuss the method creation of remote sensing dataset for estimating burned area by using Landsat-8 imagery with Python and Geographic Information System (GIS) software. The final purpose of this study is to provide the dataset of burned area considering that almost every year Indonesia experiences land fires. With the dataset for the burnt area, it is hoped that it can help respond to and handle fire disasters more quickly and efficiently. [ABSTRACT FROM AUTHOR]

The aim of this paper refers to obtaining accurate satellite image data by remote sensing and image interpretation, regarding the cultivated agricultural land declared by farmers from the territory of Romania. The remote sensing control activity is structured on control areas, comprising of an area of 23.520km2, distributed into 28 zones, divided into 2 lots, covering the entire territory of Romania. The satellite images were obtained from three sets of measurements, respectively in March, June and August. For each set an image VHR (Very High Resolution) and 2 images HR (High Resolution) were obtained. On these satellite images, the limits of the parcels, the area, the type of crop, the cultivation mode with relation to the contour map have been established. In this paper, we will highlight the importance of new technologies and their impact on agriculture by presenting a practical application. [ABSTRACT FROM AUTHOR]

The marshes of Mesopotamia are distinguished by their environmental and biological diversity and of economic and natural importance as they are considered one of the largest wetlands in East Asia, and significant changes have occurred in them during the last period, this paper aims to know and determine the extent of the changes in the land cover and the main features (water, cane, plant, soil) for AlHammar marsh in southern Iraq, and to achieve this object, the Landsat 8 satellite images was used for the years (OLI) 2018,2019. And remote sensing techniques were adopted here using the three-way supervised classification technique (maximum likelihood, minimum distance, parallelepiped) and applied Three color combinations Various are (432), (451) and (452) through the (ENVI) program, and also the (ArcGIS 10.4.1 "arcMap") program was used to cut off the study area and to extract maps, the results showed that there were major changes in the flooded areas of the marshes. [ABSTRACT FROM AUTHOR]

The paper presents results of the spectral brightness study of coniferous and foliar forests on the satellite images Landsat-7 for different seasons. Spectral channels, in which reflections from such species most differ from each other, are determined. The obtained results allow one to form standards for the classification of forest vegetation cover in different phenological phases. To increase the distinctiveness of the analyzed objects, information about their brightness was merged for different combinations of two spectral channels. This allows one to form two additional classification features: the Euclidean distance and spectral angle in two-dimensional brightness space. It is shown that the combination of two channels can significantly increase the number and quality of the formed features for the selection of coniferous and foliar vegetation. [ABSTRACT FROM AUTHOR]

Remote sensing of the Earth allows to receive information of medium, high spatial resolution from space vehicles and to conduct hyperspectral measurements. A model of the automated space monitoring system (ASMS) for the presence of waste disposal facilities (WDFs) is being developed in this paper. One of the components of the model is the space imagery storage unit, which allows not only to improve the performance of environmental authorities, but also to improve the effectiveness of monitoring the WGF (space, aerological and ground). The article also investigates the problem of search of similarity in archives of space images based on Haar wavelet transformations for stability. [ABSTRACT FROM AUTHOR]

Indonesia is located in an area prone to disasters, which are various kinds of natural disasters happen. In disaster management, the geoinformation data are needed to be able to evaluate the impact area. The UAV (Unmanned Aerial Vehicle)-Based remote sensing technology is a good choice to produce a high spatial resolution of less than 15 cm, while the current resolution of the satellite imagery is still greater than 50 cm. This paper shows some technical notes that should be considered when using UAV-Based remote sensing system in post disaster for rapid assessment. Some cases are Aceh Earthquake in years 2013 for seeing infrastructure damages, Banjarnegara landslide in year 2014 for seeing the impact; and Kelud volcano eruption in year 2014 for seeing the impact and volumetric material calculation. The UAVBased remote sensing system should be able to produce the Orthophoto image that can provide capabilities for visual interpretation the individual damage objects, and the changes situation. Meanwhile the DEM (digital Elevation model) product can derive terrain topography, and volumetric calculation with accuracy 3-5 pixel or sub-meter also. The UAV platform should be able for working remotely and autonomously in dangerous area and limited infrastructures. In mountainous or volcano area, an unconventional flight plan should implemented. Unfortunately, not all impact can be seen from above such as wall crack, some parcel boundaries, and many objects that covered by others higher object. The previous existing geoinformation data are also needed to be able to evaluate the change detection automatically. [ABSTRACT FROM AUTHOR]

The option to introduce spaceborne data that enables to recognize spatial patterns, to enhance ground-collected data, to acquire high-resolution spatial and spectral data, and to incorporate this data into a Geographic Information Systems (GIS), has been recently recognized and used by the geological research community. The current paper presents the development of a semi-automated model for geological mapping and 3D geodata investigation based on a set of image processing and GIS techniques. The aim of the developed methodology is to extract lithological and structural information by combining geological object recognition of hyperspectral spaceborne data and GIS data mining and analysis. Input data consist of a Digital Terrain Model (DTM) and a hyperspectral satellite image. These data serve as the basis for a supervised classification of the geological units and for extracting geostructural data in order to construct a 3D database of the area's geology. The output is stratigraphic information (such as dip and strike) that can be used for mapping and constructing 2.5D/3D models of the subsurface or, in conjunction with additional thematic layers, for geological spatial analysis. [ABSTRACT FROM AUTHOR]

In this paper we present a new lossless algorithm for compression of the signals from advanced hyperspectral infrared sensors onboard sun- and geo-synchronous environmental satellites. At each stage, our compression algorithm achieves an efficient grouping of channel data points around a relatively small number of 1-dimensional lines in a large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points via adaptive clustering. Using one full day’s worth (24 h) of global hyperspectral data obtained by the AQUA-EOS Atmospheric Infrared Sounder (AIRS), the mean ratio of compression attainable by the method is shown to be ≃ 3.7 to 1. © 2006 American Institute of Physics [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important parameter to optimize water use, especially in non-irrigated and arid or semiarid rangelands. A simplified method to derive the daily evapotranspiration using remote sensing images of high and low spatial resolution is presented in this paper. The method is based on the evaporative concept employed in the S-SEBI (Simplified Surface Energy Balance Index) model, and it is applicable under the assumption of constant atmospheric conditions and sufficient wet and dry pixels over the image. The method, applied to DAIS and NOAA-AVHRR images, has been tested using ET values measured over the agricultural area of Barrax (Castilla-La Mancha, Spain), and an accuracy for the daily evapotranspiration lower than 1 mmd-1 has been obtained.. © 2006 American Institute of Physics [ABSTRACT FROM AUTHOR]