Collaborative Coupled Hyperspectral Unmixing Based Subpixel Change Detection for Analyzing Coastal Wetlands

Abstract

Owing to the complicated and heterogeneous distribution characteristics of wetland features, the existing hyperspectral technology is difficult to investigate the inner-pixel subtle changes. In this paper, we present a sub-pixel change detection method based on collaborative coupled unmixing (SCDUM) for monitoring coastal wetlands. A novel multitemporal and spatial scale collaborative endmember extraction method based on joint spatial and spectral information is proposed. In the proposed method, the multitemporal hyperspectral images are firstly jointly clustered and segmented based on multi-feature fusion of spectral features, texture features, and shape features. Then a different spatial scale non-negative matrix factorization based on original and down-sampled multitemporal hyperspectral images is proposed to accurately extract the pure endmembers of each segmented images. Finally, the global abundance of the multitemporal image is effectively estimated for change detection. In addition, in order to verify the accuracy of the change detection results without reference, an accuracy verification strategy by using high spatial resolution Sentinel-2A image as auxiliary data is implemented. The Yellow River Estuary coastal wetlands was selected as the research area, and the Gaofen (GF)-5 and ZY-1 02D hyperspectral images were used as the research data. In particular, the proposed method not only provides the overall change information, but also obtains the component of change direction and intensity of each kind of endmember, and the experimental results shows that the SCDUM gives more accurate detection results, with closer to the endmember spectral curves of real objects, compared with other state-of-the-art methods.

Subpixel Change Detection Based on Improved Abundance Values for Remote Sensing Images

“…Wu et al [28] developed a new LCCD approach based on spectral unmixing from stacked multi-temporal remote sensing images with variable endmembers. For monitoring coastal wetlands, a subpixel level LCCD approach via collaborative coupled unmixing using spatial and spectral information is presented in [29]. In [30], a new LCCD method based on convolutional sparse analysis and temporal spectral unmixing was proposed to combine the advantages of pixel-and subpixel-level change detection.…”

“…For the above problems, a large number of attempts by researchers have been made in recent years, such as spatiotemporal fusion-based methods [23][24][25][26], spectral unmixing-based methods [27][28][29][30], and subpixel mapping-based methods [31][32][33][34]. Next, we will make a brief introduction to these LCCD methods.…”