Automation in photogrammetry: Recent developments and applications (1972–1976)

Thompson, Morris M.; Mikhail, Edward M.

doi:10.1016/0031-8663(76)90008-9

Cited by 9 publications

(8 citation statements)

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“…In the late 1970s, digital image analysis by computers was carried out only in specialized research institutions; personal computers and many remote sensing software packages which are now available, did not exist [6,11]. The development of remote sensing technology advanced in line with the development of GIS which provided the platform for bringing remote sensing data and other geospatial information into a common framework [5,6].…”

Section: Early Landsat Digital Land Covers Classification Principlesmentioning

confidence: 99%

“…The most common types of similarities used in pattern recognition are based on correlation and Euclidean distance between objects [6]. In classification, these techniques may be used as a single technique; however, Steiner [5] reported that a combination of the two techniques produces superior Figure 1.…”

Section: Early Landsat Digital Land Covers Classification Principlesmentioning

confidence: 99%

“…The numerical classification approach is one of the most important developments in pattern recognition and is the foundation of modern land cover classification methods [11,40]. Generally, pattern recognition employs similarities between objects in classification of land cover types [6]. Modern classification methods were developed from the early pattern recognition techniques and were implemented on computer-automated programs through machine learning and artificial intelligence theories.…”

Section: Early Landsat Digital Land Covers Classification Principlesmentioning

confidence: 99%

“…Other important aspects of pattern recognition used in classification are discrimination and grouping techniques. Discrimination techniques are useful in establishing boundaries between patterns, which were recognized based on similar properties [5,6]. These methods employ linear or non-linear transformation methods depending on the normal distribution of the data involved.…”

Section: Early Landsat Digital Land Covers Classification Principlesmentioning

confidence: 99%

“…With the Landsat program running for over four decades now, different methods for classifying land cover were developed. The development of these methods was largely attributed to the improvements in Landsat images, advancement of computer technology, development of geographic information systems (GIS) and the Landsat free access policy [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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