Pen culture detection using filter tensor analysis with multi-temporal landsat imagery

Abstract

Aquaculture plays an important role in China's total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases.