Heterogeneously sensed imagery radiometric response normalization for citrus grove change detection

Citrus grove change detection is of great importance to citrus production inventory monitoring. Using remotely sensed imagery to detect the land use and land coverage is one of the most widely-used, cost-effective approaches. However, there is little published research on citrus grove change detection using remotely sensed multi-spectral imagery, especially for those acquired by heterogeneous sensors. The purpose of this paper is to investigate the effectiveness of the citrus change detection based on the histogram matching normalization to the heterogeneously sensed imagery. In this paper, it is found that different reference image and band selection will result in different normalization performance. Based on this finding, a concept of finding optimal reference image and best spectral band for normalization in terms of the minimum Manhattan distance measure is presented. In this paper, the comparison of change detection results of unnormalized and histogram matching normalized images is presented. The experimental results show that histogram matching normalization significantly improves the image differencing based change detection results of the heterogeneously sensed citrus images, and the optimal reference image and band found with proposed optimization algorithm gives the best change detection results.