A New Deep Convolutional Network for Effective Hyperspectral Unmixing

Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms to complete the abundance estimation step. Due to the impressive learning and data fitting capabilities of convolutional neural networks (CNNs), deep learning (DL)-based hyperspectral unmixing technologies have rapidly developed in the literature. According to the procedure used to combine different layers (i.e., fully connected layers, convolution layers, and activation layers), these techniques are mainly divided into three main categories, i.e., those based on autoencoder networks, convolutional neural networks, and convolutional autoencoder networks. They usually extract the weight and output of a specific activation layer as endmember signatures and abundance maps, respectively. Moreover, most existing DL-based unmixing approaches usually use 2-D CNNs to learn the features contained in hyperspectral images, and very few approaches employ 3-D CNNs to extract spectral and spatial information. However, 2-D CNN-based techniques cannot capture good discriminative feature maps from the spectral viewpoint, and 3-D CNN-based techniques usually have high computational overload. In this work, to further exploit the feature extraction capability of CNNs, we combine 3- and 2-D convolutions to propose a cross-convolution unmixing network (CrossCUN) for hyperspectral unmixing. Simultaneously, to better illustrate the improvements of our proposed CrossCUN, we also build the corresponding 2-D convolution unmixing network (2-DCUN) and 3-D convolution unmixing network (3-DCUN). We evaluate the performance of our newly developed networks on two types of synthetic datasets and three real hyperspectral images. Experimental results show that the proposed networks not only obtain better results than other DL-based unmixing methods but also do not require any prior knowledge (e.g., the results of other endmember extraction algorithms) to estimate the abundance maps.