Identification of high-temperature targets in remote sensing based on correspondence analysis

Abstract

Abstract High temperature targets (temperature above 500 K), are the special on the surface of the earth such as forest fire, prairie fire, oil well torches, heap coking, volcanic eruptions, significantly different from those of normal surfaces at lower temperatures. Identification of high-temperature targets plays an important role in environmental monitoring, disaster warning, and resource investigation. In remote sensing data, high-temperature target pixels and bands are studied. And they are deemed samples and variables, respectively, in multivariate analysis. And classification of samples for identification of high-temperature targets is necessary. To classify samples, feature analysis of spectrum needs to be done first. In feature analysis of spectrum, feature bands that can be used to distinguish samples need to be selected. Correspondence analysis is the method that can project samples and variables into the same factor space in the meantime. It can realize the classification of samples and variables synchronously, and the results can be interpreted by each other. First, the correspondence analysis is conducted on Landsat8/OLI remote sensing imagery to build the relationship between samples and variables. After that the correspondence relationship between identification results of high-temperature targets and feature bands can be built in the physical theory of remote sensing and factors which have indicative significance on fire are confirmed. Finally, the single band threshold method is adopted to realize high temperature target recognition by using factor scores. In the field confirmation, results suggest that the precision of identification of high-temperature targets reaches 92%. And we also get a consistent result with SWIR temperature inversion.