A Review of Remote Sensing Image Classification Techniques.

European Journal of Remote Sensing - 2014, 47: 389-411 Image classification methods Pixel-wise image classification As the classic remote sensing image classification technique, pixel-wise classification methods assume each pixel is pure and typically labeled as a single land use land cover type (Fisher, 1997; Xu et al., 2005) (see Tab. 1).

In this article we will learn what is classification and basic concepts of supervised classification. We will also discuss briefly about various types of supervised classification algorithms used for classifying remote sensing images.

Chapter 15: Remote Sensing - GIS-Lab.

Remote Sensing Introduction to image classification. Remote Sensing is the practice of deriving information about the earth’s surface using images acquired from an. remote sensing system is an active sensor that sends out a beam of light with a known wavelength and frequency.Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object and thus in contrast to on-site observation, especially the Earth. Remote sensing is used in numerous fields, including geography, land surveying and most Earth science disciplines (for example, hydrology, ecology, meteorology, oceanography, glaciology, geology); it.Thus, remote sensing data can be integrated into physical equations of energy-balance. An aerial photograph is a central projection, with the whole picture taken at one instance. A remote sensing image is created line after line, so the geometrical correction is much more complex, with each pixel needing to be treated as a central projection.


With this special issue we compile state-of-the-art analysis methods for converting remote sensing image data into information relevant to various earth sciences and monitoring applications. We assume that the remote sensing image data has undergone radiometric and geometric correction processing.Abstract: More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features.

Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming.

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Remote sensing in geology is remote sensing used in the geological sciences as a data acquisition method complementary to field observation, because it allows mapping of geological characteristics of regions without physical contact with the areas being explored. About one-fourth of the Earth's total surface area is exposed land where information is ready to be extracted from detailed earth.

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Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

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Image Classification Many remote sensing systems record brightness values at different wavelengths that commonly include not only portions of the visible light spectrum, but also photoinfrared and, in some cases, middle infrared bands. The brightness values for each of these bands are typically stored in a separate grayscale image (raster).

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J.R. AndersonLand use classification schemes used in selected recent geographic applications of remote sensing Photogrammetric Engineering, 37 (4) (1971), pp. 379-387 Google Scholar.

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Remote sensing—the acquisition of information from a distance—has had a profound impact on human affairs in modern history. This image of British Beach (the WWII code name for one landing spot of the June 1944 Normandy invasion) taken from a specially equipped US Army F5, reveals rifle troops on the beach coming in from various large and small landing craft.

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This image shows the use of training sites, shown as colored polygons, to inform the remote sensing software of major land cover and vegetation classes in the image for a supervised classification (image source: Short, N. 2009. The Remote Sensing Tutorial, Section 1).

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Definition of Remote Sensing: Remote Sensing can be defined as the science and art of acquiring information about an object made from a distance without physical contact with the object. In practice, remote sensing is the utilization at a distance (as from aircraft, spacecraft, satellite, or ship) of any device for gathering information about the environment.

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LOGO What is Image Classification? Remote sensing is the science and the art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not contact with the object, area or phenomenon under investigation.(Lillesand and Kiefer, 1994).

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OBIA is beyond the scope of this chapter, but you can study it in depth in the open-access Penn State courseware GEOG 883: Remote Sensing Image Analysis and Applications. Pixel-based classification techniques are commonly used in land use and land cover mapping from imagery.

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