Abstract
Convolutional neural network (CNN) is well-known for its powerful capability on image classification. In hyperspectral images (HSIs), fixed-size spatial window is generally used as the input of CNN for pixel-wise classification. However, single fixed-size spatial architecture hinders the excellent performance of CNN due to the neglect of various land-cover distributions in HSIs. Moreover, insufficient samples in HSIs may cause the overfitting problem. To address these problems, a novel divide-and-conquer dual-architecture CNN (DDCNN) method is proposed for HSI classification. In DDCNN, a novel regional division strategy based on local and non-local decisions is devised to distinguish homogeneous and heterogeneous regions. Then, for homogeneous regions, a multi-scale CNN architecture with larger spatial window inputs is constructed to learn joint spectral-spatial features. For heterogeneous regions, a fine-grained CNN architecture with smaller spatial window inputs is constructed to learn hierarchical spectral features. Moreover, to alleviate the problem of insufficient training samples, unlabeled samples with high confidences are pre-labeled under adaptively spatial constraint. Experimental results on HSIs demonstrate that the proposed method provides encouraging classification performance, especially region uniformity and edge preservation with limited training samples.
Highlights
With the rapid development of hyperspectral sensors, hyperspectral remote sensing images have become more available
We investigate the performance of the proposed method from the following aspects: classification performance, running time, sensitivity analysis to the number of training samples, and sensitivity analysis of free parameters
Compared with stacked autoencoder (SAE) and deep belief network (DBN), Convolutional neural network (CNN), PPF-CNN, 3DCNN, and divide-and-conquer dual-architecture CNN (DDCNN) are superior by making full use of the spatial information in Hyperspectral images (HSIs)
Summary
With the rapid development of hyperspectral sensors, hyperspectral remote sensing images have become more available. The detailed spectral information provided by hyperspectral sensors improves the capacity to differentiate the interesting land-cover classes It makes HSI classification one of the most promising techniques in many practical applications, including agriculture [2], military [3], astronomy [4], mineralogy [5], surveillance [6], and environmental sciences [7,8]. In the early stage of HSI feature extraction, various spectral-based methods were proposed, such as principal component analysis (PCA) [10,11], independent component analysis (ICA) [12,13], manifold learning [14], sparse graph learning [15], and local Fisher's discriminant analysis (LFDA) [16] These methods are implemented by transforming original high-dimensional data into an appropriate low-dimensional space. In one study [23], the kernel low-rank multitask method is proposed to capture multiple features from the 2-D variational mode decomposition domain for multi-/hyperspectral image classification
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