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).

The Importance of Remote sensing imagery Interpretation & spectral analysis

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).


The Importance of Remote sensing imagery Interpretation & spectral analysis

Remote sensing imagery Interpretation & spectral analysis (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).

Decoding of RS 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.

Работник

Objectives and Tasks of Remote sensing imagery Interpretation & spectral analysis:

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.

Advantages of Using Remote Sensing Data for remote sensing images spectral analysis:

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.


Prices for services

Consultation Free of charge
Decoding of remote sensing images spectral analysis The cost of the work is calculated individually for each order and may vary depending on the complexity and volume of the work.
Work of Technical Specialists and Experts Starting from 100,000 rubles
TOTAL COST Starting from 100,000 rubles

The cost depends on:

  • Area of the area of interest (work area);
  • Number of images;
  • Quality characteristics of the images;
  • Complexity of the terrain;
  • Seasonality of work;
  • Size of the advance payment;
  • Task complexity;
  • Requirements for computational power;
  • And more.

The cost of execution is calculated on an individual basis, taking into account a specific of task.

After receiving the task description, we calculate the cost and send you a commercial offer.

Period of execution

Completion time for decoding remote sensing images spectral analysis is approximately 10 working days from the date of receiving the advance payment and is calculated individually for each customer.

The completion time of the work depends on:

  • Total area of the area of interest;
  • Requirements for the final result.

The service delivery time depends on the complexity of the work and is calculated individually for each customer.


How to place an order:

Step №1: Submit an application on the website with the following information:

  • Location of the research object (coordinates);
  • Questions;
  • Dates for the decoding to be conducted.

Step №2: Agreement on technical specifications and cost:

  • Research starting from 100,000 rubles;

Step №3: Contract signing and start of work:

  • Completion time is approximately 20 working days from the date of receiving the advance payment - payment is accepted only through non-cash transactions.

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Stages of service provision

Stage № 0 (BEFORE contract conclusion):

  • Receipt and coordination of data from the Customer. It is necessary to coordinate the task requiring a solution, the size, nature of the area, and the requirements for creating the product to calculate the cost and timeframe for the work.

RESULT: possibility (YES/NO) of providing the service

Stage № 1 (BEFORE contract conclusion):

  • Agreement on technical specifications
  • Final determination of labor costs, agreement on deadlines, and costs

RESULT: signed contract

Stage № 2 (contract execution):

  • Processing of remote sensing data (DZZ);
  • Decoding of remote sensing data (DZZ);
  • Verification of the obtained result;
  • Preparation of the final report.
  • RESULT: Transfer of materials to the Customer.

The result of the provision of services

Stage № 0 (BEFORE contract conclusion):

  • Receipt and coordination of data from the Customer. It is necessary to coordinate the task requiring a solution, the size, nature of the area, and the requirements for creating the product to calculate the cost and timeframe for the work.

RESULT: possibility (YES/NO) of providing the service

Stage № 1 (BEFORE contract conclusion):

  • Agreement on technical specifications
  • Final determination of labor costs, agreement on deadlines, and costs

RESULT: signed contract

Stage № 2 (contract execution):

  • Processing of remote sensing data (DZZ);
  • Decoding of remote sensing data (DZZ);
  • Verification of the obtained result;
  • Preparation of the final report.
  • RESULT: Transfer of materials to the Customer.

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Customers

FAQ

Primary snapshots:

  • acceptable accuracy of spatial geolocation of images;
  • spectral range of shooting: panchromatic, multispectral (color), color with near-infrared channel, color with short-wave near-infrared channel;
  • the angle of deviation of the image from the nadir, for example, up to 20 degrees or up to 10 degrees;
  • radiometric resolution of images, for example, 8 bits /16 bits (the higher the resolution, the more brightness gradations can be seen in the image);
  • the percentage of clouds in the image, for example, is no more than 20%;
  • snow cover in the images (20% of the area).

If space image processing is required, the requirements are discussed with the Contractor.
For high spatial resolution (0.15 – 1.0 meter), images from spacecraft (0.3 – 1.0 meter) Maxar, Planet, AirBus Defense, Chinese: SuperView-1 (GaoJing-1), Jilin series - KF-01B, L-SAR 01A and L-SAR 01B, Russian "Resource-P". Their survey solves the problems of detailed decoding of built-up areas of cities, buildings, structures, structures, land and forest areas (details of vegetation changes), streets, squares, avenues, roads, utilities and regime zones and other lands on a cartographic scale of 1: 2000-1:10,000. For medium resolution (2-7 meters), high-quality shooting of the Spot 6,7 spacecraft, the Japanese ALOS series, the South Korean Kompsat spacecraft series, the relatively satisfactory quality of the Russian Canopus series. Scale 1:25 000- 1: 200 000. Such a survey helps to solve the problems of decrypting water bodies, agricultural land and use, rather long sections. And finally, the resolution from 15 to 60 meters of the Landsat and Aster spacecraft, but the thematic solution for decoding vegetation and land is an order of magnitude higher than those mentioned above, so they have up to 16 filters of optical and IR ranges, including thermal. Analytical combined processing allows you to pull out, if necessary, and high spatial resolution, but with high-quality data, for example, the Landsat spacecraft.
Yes, of course, if you need to increase the detail of the object under study, pull out structural features from the image of the object, create 3D models, an overview of 3600, a multimedia product, get an oblique shooting and texture of the image.
remote sensing images spectral analysis of multi-stage processing of satellite images of a comparative nature for the same area over a certain time period (5-10 years) with the decryption of the features of changes in urban development, road network, electrical network, telecommunications infrastructure, soil, land, vegetation, water resources.
The most modern methods of decryption, tested in practice, are the methods of so-called "artificial intelligence" (machine reading), based on the selection of objects from neural networks, automatically comparing prepared standards, installed objects, with objects in the image. However, not everything is so simple. There is a dataset of satellite images for object detection using convolutional neural network-based frameworks such as faster RCNN (faster convolutional neural network based on regions), YOLO (you only look once), SSD (single snapshot detector) and SIMRDWN (multiscale fast detection of satellite images with windowed networks). In addition, there is an analysis of these approaches in terms of accuracy and speed using the developed dataset of objects on satellite images.
Interpreting remote sensing images involves the analysis and comprehension of data captured by sensors on satellites and other platforms. These images offer valuable insights into the Earth's surface, atmosphere, and oceans, providing essential information for various applications such as environmental monitoring, land use/land cover mapping, urban planning, disaster management, agricultural assessment, and more.
When remote sensing images interpretation assess the spectral characteristics of the captured data, which refer to the reflectance or emission of light at different wavelengths. By examining these spectral signatures, analysts can identify materials, surface features, and environmental conditions. For example, vegetation appears differently in the near-infrared spectrum than built-up areas or water bodies, allowing for differentiation and classification.
Understanding remote sensing images interpretation requires interdisciplinary knowledge spanning geography, geology, environmental science, computer science, and data analysis. As technology continues to advance, the interpretation of remote sensing images will play a pivotal role in addressing global challenges and informing evidence-based decision-making across various fields.
Remote sensing imagery spectral analysis involves studying the electromagnetic radiation reflected or emitted from the Earth's surface or atmosphere, captured by sensors aboard remote sensing platforms such as satellites or aircraft. This analysis is fundamental to understanding how different materials and features interact with light across various parts of the electromagnetic spectrum. By examining these interactions, analysts can derive valuable information about the characteristics and composition of the Earth's surface, vegetation, water bodies, and atmosphere.
Remote sensing imagery spectral analysis involves involves considering how materials and objects reflect or emit electromagnetic radiation at different wavelengths. This data is typically captured in several spectral bands, each corresponding to specific ranges of the electromagnetic spectrum, such as visible, near-infrared, shortwave infrared, and thermal infrared, among others.
For instance, sensing imagery spectral analysis show healthy vegetation strongly reflects near-infrared radiation while absorbing more red light. This behavior results in a distinct spectral signature, enabling the identification and mapping of vegetation cover. Similarly, materials such as water, soil, urban infrastructure, and various land cover types exhibit unique spectral characteristics that can be exploited for discrimination and classification.
Spectral analysis also encompasses techniques like spectral unmixing, which aims to deconstruct mixed spectral signals in a pixel into their constituent parts. This approach is valuable for discriminating and quantifying the composition of land cover types or surface materials within a single pixel, even when multiple materials contribute to the observed spectral response.
Remote sensing imagery interpretation involves visually analyzing satellite or aerial images to extract meaningful information about the Earth's surface. It plays a key role in identifying and categorizing features, understanding land cover changes, and supporting various applications in environmental monitoring, agriculture, and urban planning.
Spectral analysis in remote sensing involves examining different bands of the electromagnetic spectrum captured by sensors. Each band provides unique information about the surface. For example, visible and near-infrared bands are useful for vegetation health analysis, while thermal infrared bands can reveal temperature variations. Spectral analysis aids in identifying specific features and understanding their characteristics.
Challenges in interpreting remote sensing imagery include human subjectivity, complex landscapes, and the vast amount of data to analyze. Advanced technologies, including machine learning algorithms, assist in automating the interpretation process by training models to recognize patterns and features in the imagery. This automation improves efficiency and can enhance accuracy in image interpretation.
Multispectral imagery captures data in multiple bands of the electromagnetic spectrum, allowing for a more detailed interpretation of the Earth's surface. It is valuable in applications such as land cover classification, vegetation health assessment, and environmental monitoring. The different bands provide diverse information that aids in distinguishing between various surface features.
Remote sensing imagery interpretation supports decision-making processes by providing valuable insights for different sectors. In agriculture, it helps monitor crop health and optimize farming practices. In forestry, it aids in assessing forest cover and planning sustainable management. In urban planning, it contributes to land-use mapping and infrastructure development. These interpretations inform evidence-based decision-making in various fields.

Licenses

Warranty

Resolution of the Government of the Russian Federation: of June 10, 2005, No. 370 "On the Approval of the Regulation on Planning of Space Imaging, Reception, Processing, Storage, and Distribution of Earth Remote Sensing Data from Civilian Spacecraft with High (less than 2 meters) Resolution" (with amendments and additions); of July 7, 2015, No. 682 "On the Powers of Federal Executive Authorities in the Field of Using the Results of Space Activities"; three orders of "Roscosmos" on the Federal Fund of Earth Remote Sensing Data from Space; six Russian Government Resolutions on the Federal Fund of Earth Remote Sensing Data from Space.

As of March 30, 2022, 36 Remote Sensing Data Standards have been approved by orders of Rosstandart.

At the international level, remote sensing activities are regulated by the following foundational acts:

  • Convention on the Transfer of Data and the Use of Remote Sensing Data from Space (concluded in Moscow, May 19, 1978).
  • Principles relating to Remote Sensing of the Earth from Space (adopted by Resolution 41/65 of the United Nations General Assembly on December 3, 1986).

We guarantee that all work will be carried out in accordance with SNiP, GOST, and SP., using advanced methodologies and state-of-the-art software.

We ensure 100% service quality. By collaborating with GEO Innovations specialists, you eliminate risks and losses.

Our qualified personnel, experienced in working with specialized software, allows us to provide these guarantees!


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