TARGETED LAND-COVER CLASSIFICATION by: Shraddha R. Asati Guided by: Prof. P R.Pardhi

OVERVIEWS: Introduction Land-cover classification Image classification Classification strategies Features of TLCC Problems with current classification systems A new approach to classification Issues The advantages of the method adopted Conclusion References

Land cover is the observed physical cover on the earth's surface. When considering land cover in a very pure and strict sense it should be confined to describe vegetation and man-made features. Areas where the surface consists of bare rock or bare soil are describing land itself rather than land cover. Also, it is disputable whether water surfaces are real land cover. Land Cover INTRODUCTION

Land-cover classification, aiming at mapping the different land-cover typologies characterizing a certain geographic area at a given time, represents one of the main application areas of satellite Earth observation technology. (e.g., agriculture, forestry, ecosystem monitoring, disaster management,etc.) The objective of land-cover classification is actually limited to map one or few specific “targeted” land-cover classes over a certain area. LAND-COVER CLASSIFICATION

IMAGE CLASSIFICATION The major steps of image classification may include

Fig: Concept of classification of remotely sensed data

There are three basic classification strategies: Supervised Classification: techniques require training areas to be defined by the analyst in order to determine the characteristics of each category Unsupervised Classification :searches for natural groups of pixels, called clusters, present within the data by means of assessing the relative locations of the pixels in the feature space Hybrid Classification: It takes the advantage of both the supervised classification and unsupervised classification. CLASSIFICATION STRATEGIES

The main features of TLCC can be outlined as follows. Objective: To map only one or few specific land-cover classes of interest (i.e., targeted classes), disregarding all the other potential classes present in the area under analysis that could be even completely unknown to the operator. Constraint: Exhaustive ground-truth information is not accessible, but exclusively training samples associated with the only class or the few classes of interest are supposed to be available. Operational Requirement: Classification accuracies should be comparable to those provided by traditional fully supervised classifiers relaying on training samples for all the classes present in the image under analysis. FEATURES OF TLCC

PROBLEMS WITH CURRENT CLASSIFICATION SYSTEMS In most current classifications, the criteria used to derive classes are not systematically applied. Factors are often used in the classification system which result in a undesirable mixture of potential and actual land cover (e.g., including climate as a parameter). The reason why most systems fail in application of this basic classification rule is that the entire set of permutations of the possible classifiers would lead to a vast number of classes which cannot be handled with the current methods of class description

A NEW APPROACH TO CLASSIFICATION land cover as the observed (bio)physical cover on the earth’s surface but, in addition, it is emphasized that land cover must be considered a geographically explicit feature which other disciplines may use as a geographical reference (e.g., for land use, climatic and ecological studies). Many current classification systems are not generally suitable for mapping, and subsequent monitoring, purposes. The integrated approach requires clear distinction of class boundaries. One of the basic principles adopted in the new approach is that a given land cover class is defined by the combination of a set of independent diagnostic attributes, the so-called classifiers

ISSUES The straightforward application of this condition is hampered by two main factors. First, land cover should describe the whole observable (bio)physical environment and therefore deals with a heterogeneous set of classes. Secondly, two distinct land cover features, having the same set of classifiers to describe them, may differ in the hierarchical arrangement of these classifiers in order to ensure a high mapability.

THE ADVANTAGES OF THE METHOD ADOPTED It is a real a priori classification system. The classification is truly hierarchical. The classes derived from the proposed classification system are all unique and unambiguous.

The TLCC is applicable for areas like agriculture, forestry, ecosystem monitoring, disaster management, etc very efficiently. The TLCC technique gives accuracies greater than existing system. CONCLUSION

REFERENCES Mattia Marconcini, Diego Fernández-Prieto, and Tim Buchholz “Targeted Land-Cover Classification”, IEEE Trans. Geosci. Remote Sens., VOL. 52, NO. 7, JULY 2014 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York, NY, USA: Wiley, L. Samaniego, A. Bardossy, and K. Schulz, “Supervised classification of remotely sensed imagery using a modified k-NN technique,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2112–2125, Jul