NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing

Abstract

Normalized difference vegetation index (NDVI), derived from multi-spectral (MS) images, is a metric widely used to evaluate the growth status of vegetation in remote sensing. Existing methods for generating high-resolution (HR) NDVI are typically based on pan-sharpening, which often result in huge errors even in case of tiny spectral distortions. To overcome this challenge, from a novel perspective, this paper introduces an HR vegetation index (HRVI) to realize direct fusion with a low-resolution NDVI rather than pan-sharpening an HRMS image. In particular, we propose a two-branch network based on the multi-scale and attention mechanism, termed as NDVI-Net, to obtain the HRNDVI with small distortion. In our network, the multi-scale channel enhancement blocks are used in both NDVI and HRVI branches, in which multi-scale convolution is used to capture structural information with different reception fields and channel attention mechanism is adopted to perform feature selection. Meanwhile, the spatial features are injected unidirectionally from the HRVI into NDVI branches, so as to further improve the quality of features in the NDVI branch. Subsequently, the spatial intensify block is adopted only in the NDVI branch to implement selective enhancement for the previously obtained features along the spatial position, strengthening the retention of local detail features. Finally, HRNDVI is reconstructed based on the high-representation NDVI features, which contains clear texture details and precise intensity. Experimental results demonstrate the significant advantage of our method over the current state-of-the-art in terms of both subjective visual effect and quantitative metrics. Moreover, we apply the HRNDVI generated by our method to vegetation detection and enhancement, and land cover mapping in remote sensing, which can achieve the best performance.