Remote sensing imagery Interpretation & spectral analysis - is the process of recognizing objects and territories, their properties, and interrelations based on their images in the captured satellite or aerial imagery.

Decoding of can be done through fieldwork or through office work. Office decoding is further divided into visual and automated decoding. Visual decoding is performed manually by an interpreter who visually identifies and decodes the objects on the image.

Automated decoding (machine decoding) is performed by an interpreter using specialized software and algorithms. Machine decoding involves various methods that group objects based on certain decoding features and essentially boils down to different classification mechanisms. Image classification can be categorized as supervised classification (Minimum Distance Method, Spectral Angle Method, Mahalanobis Distance Method) and unsupervised classification (ISODATA Method, K-Means Method).

Decoding remote sensing materials (satellite and aerial images) is conducted to obtain information about the spatial distribution of geographic objects, their occupied areas, and to identify the dynamics and characteristics of such objects.

Depending on the tasks to be accomplished, decoding of satellite images can be classified as general decoding (comprehensive or geographic) and specialized decoding (thematic or specific).

Interpretation & spectral analysis satellite images involves preliminary and main stages, which include data processing, brightness normalization for different object types, creation of mosaic coverages, etc.

The results of decoding are documented in graphical, digital, or textual formats.

Remote sensing imagery Interpretation & spectral analysis is the process of recognizing objects and territories, their properties, and interrelations based on their images in the captured satellite or aerial imagery.

Decoding can be done through fieldwork or through office work. Office decoding is further divided into visual and automated decoding. Visual decoding is performed manually by an interpreter who visually identifies and decodes the objects on the image.

Automated decoding (machine decoding) is performed by an interpreter using specialized software and algorithms. Machine decoding involves various methods that group objects based on certain decoding features and essentially boils down to different classification mechanisms. Image classification can be categorized as supervised classification (Minimum Distance Method, Spectral Angle Method, Mahalanobis Distance Method) and unsupervised classification (ISODATA Method, K-Means Method).

Decoding remote sensing images spectral analysis Objective: obtaining comprehensive information about objects and territories on the image obtained by remote sensing methods, including: type (house, street, road, field, forest, water, etc.), purpose, geometric dimensions, changes over time, detailed features and their composition, attributes.

For example, topographic decoding of images is performed with the aim of detecting, recognizing, and obtaining characteristics of objects that should be shown on a topographic map. Topographic decoding is one of the main processes in the technological scheme of map creation and updating.

Today there are many reasons why decoding of remote sensing data is considered a reasonable and even mandatory image processing procedure; otherwise, it remains just a picture to contemplate.

Decoding remote sensing images spectral analysis Tasks (main ones):

• Creation of cartographic materials.
• Need for inventory of changes in the terrain.
• Obtaining extensive spatial coverage when creating a medium-scale updatable map.
• Need to determine and cartographically display special characteristics of objects.
• Cartographic representation of objects not marked on topographic or other specialized maps (due to insufficient accuracy).
• Composition and changes of agricultural fields, forests, urban infrastructure.
• Geological structures.
• Condition of water bodies.
• Ecological aspects of natural environment composition and changes.
• Other processes involved in thematic mapping according to the Client's request.

Specialists in the field choose a comprehensive approach to decoding remote sensing data, thanks to their experience and program-technical equipment.

If we follow the path of creating values for decoding panchromatic and multispectral data - a multi-purpose index used to measure the spectral quality and spatial detail of generated images, we will achieve significant improvement in quality (realism of objects and territories) compared to the results of classical approaches to decoding the details of generated images.

The multi-purpose index is also influenced, for example, in agricultural and forestry applications, by the composition of vegetation, its density, condition, to a lesser extent, exposure, and surface slope. For example, this is the NDVI index.

In thematic cartography, sensors with medium spatial resolution (1.5-5.0 m) are used to a greater extent. Such resolution finds application in the vast majority of cases when there is a need for individual information decoding over significant territories.

Modern images with lower resolution (30-100 m) are also of great interest to science and beyond. They contain a significant amount of useful information that helps solve thematic mapping tasks over large areas.

For urban infrastructure, high-resolution images (0.3 - 1.0 m) are typically decoded, while local areas are captured by aerial or UAV sensors (3 - 25 cm).

Importantly, UAVs allow for rapid decoding, including the extraction of object textures, compared to fieldwork, which may not cover height characteristics, and structural and qualitative changes can take years to study.