Using Environmental Context to Synthesis Missing Pixels

Satellites have proven to be a technology that can help in a variety of environmental and human development contexts. However, at times some pixels in the satellite images are not captured. These uncaptured pixels are called missing pixels. Having these missing pixels means that important data for research and satellite imagery-based applications is lost. Therefore, people have developed pixel synthesis methods. This paper presents a new pixel synthesis method called the Iterative Self-Organizing Data Analysis Techniques Algorithm - Integration of Geostatistical and Temporal Missing Pixels' Properties (ISODATA-IGTMPP). The method is built upon the Integration of Geostatistical and Temporal Missing Pixels' Properties (IG TMPP) method and adds a seminal clustering technique called the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The clustering technique allows a new way of predicting the missing pixel from their environmental class with benefit of the spatial and temporal properties. Here, the ISODATA-IGTMPP method was tested on the Spatial-Temporal Change in the Environment Context (STCEC) dataset and was compared with results of four missing pixel predicting methods. The method shows the best performing results and preforms very well across different environment types.