Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

Abstract

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page : https://github.com/liubing220524/MAPC-DRF-HSI. INDEX TERMS Hyperspectral image classification, morphological attribute profile cube, multi-grained scanning, deep random forest.

Deep Multiview Learning for Hyperspectral Image Classification

“…The neighborhood size is an important parameter that affects the classification performance. To analyze the influence of neighborhood size on classification accuracy, the neighborhood size is set to be 5, 7,9,11,13,15,17,19,21,23,25,27,29,31,33, and 35, respectively. The classification results are shown in Fig.…”

“…A disadvantage of these pixelwise classifiers is that it could not consider spatial information in the classification procedure. In this context, feature extraction methods that could include spatial information are introduced to improve the classification performance, e.g., Gabor filters [7], local binary patterns [8], morphological profiles [9]- [11], and wavelet [12]. A major limitation of these spatial features is that they require a great deal of tuning to get them to work well on a particular data set.…”

A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

“…(1) The results can be vectorized into a library. Deep learning models require a large number of prior sample sets [43,44], which is prohibitively expensive and laborious. In addition, the road extraction results of deep learning methods lack topological network information, and the results still need extensive intervention before the data can be stored in a database [31,45].…”

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

“…In recent years, morphological attribute profiles (MAPs) have been proven to have a strong ability to detect buildings in complex urban backgrounds, which has been one of the most effective spatial structure modelling methods for HRRS images. The morphological feature set of local area constructed by MAPs can be used to realize the multi-attribute and multi-scale expression of different ground objects, thus significantly improving the separability of buildings and other ground objects [5][6][7]. However, the following limitations must be overcome to realize high-precision, unsupervised building detection based on MAPs: (1) The potential building pixels are directly determined by the differential attribute profiles (DAPs) extracted from the differential of neighboring attribute profiles (APs), and morphological attribute profile (MAP) theory does not give a scale parameter setting using clear rules, so the requirement according to the scale of the original image is used to construct (on an adaptive basis) a reasonable parameter set.…”