Multispectral Remote Sensing Image Classification Research Articles

Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have gained improved results in remote sensing image data classification. Multispectral image classification can benefit from the rich spectral information extracted by these models for land cover classification. This paper proposes a classification model called a hierarchical convolutional recurrent neural network (HCRNN) to combine the CNN and RNN modules for pixel-level classification of multispectral remote sensing images. In the HCRNN model, the original 13-band information from Sentinel-2 is transformed into a 1D multispectral sequence using a fully connected layer. It is then reshaped into a 3D multispectral feature matrix. The 2D-CNN features are extracted and used as inputs to the corresponding hierarchical RNN. The feature information at each level is adapted to the same convolution size. This network structure fully leverages the advantages of CNNs and RNNs to extract temporal and spatial features from the spectral data, leading to high-precision pixel-level multispectral remote sensing image classification. The experimental results demonstrate that the overall accuracy of the HCRNN model on the Sentinel-2 dataset reaches 97.62%, which improves the performance by 1.78% compared to the RNN model. Furthermore, this study focused on the changes in forest cover in the study area of Laibin City, Guangxi Zhuang Autonomous Region, which was 7997.1016 km2, 8990.4149 km2, and 8103.0020 km2 in 2017, 2019, and 2021, respectively, with an overall trend of a small increase in the area covered.

A Multicenter Soft Supervised Classification Method for Modeling Spectral Diversity in Multispectral Remote Sensing Data

Due to the spectral diversity of objects within the same classes and the spectral similarity between different classes, classical fuzzy classification methods perform poorly in multispectral remote sensing image classification. The basic reason is that they often use a single spectral curve to represent a land cover type while ignoring spectral diversity characteristics. To solve this issue, this article proposes a novel multicenter supervised fuzzy classification (MCSFC) method for modeling spectral diversity in multispectral remote sensing data. Images are first split into clusters with similar sizes or volumes, namely, granularities, by a hierarchical clustering process. Second, the granularities are labeled by training samples. As a result, the centers of the granularities corresponding to one type of sample can represent the spectral diversity within one class and, thus, are treated as the multiple centers of a land cover type. The membership degree of each unlabeled sample belonging to any land cover type is determined by the shortest distance to the centers of each type, and the spectral diversity is considered in this stage. A comparison reveals that the proposed method clearly improves the classification performance of various single-center fuzzy semisupervised clustering (FSSC) methods. Two case studies indicate that the granularity volume parameter notably affects the classification performance, and the overall accuracy (OA) decreases with increasing granularity volume.

Ensemble of Adaptive Rule-Based Granular Neural Network Classifiers for Multispectral Remote Sensing Images

Information granulation opens ample scope to design likely transparent neural networks called granular neural networks (GNNs). The paper proposes a classification model in the framework of ensemble of GNN-based classifiers, and justifies its improved performance in classifying land use/cover classes of multispectral remote sensing (RS) images. The model also provides an adaptive method for fuzzy rules extraction from the fuzzified input variables for GNN and thus avoid the uncertainty in empirical search of rules for output class labels. The superiority of the proposed model to other similar methods is established both visually and quantitatively for land use/cover classification of multispectral RS images. Comparative analysis revealed that GNN with multiple rules performed better than GNN with single rule assigned for each of the classes, and ensemble of GNNs outperformed all other methods. Various performance measures, such as overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and measure of dispersion estimation, are used for comparative analysis.

Improved Subspace Classification Method for Multispectral Remote Sensing Image Classification

Improved Subspace Classification Method for Multispectral Remote Sensing Image Classification