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Abstract
Motivation in the use of semisupervised learning method is because of its ability to strategically explore and use abundantly available unlabeled samples along with the limited number of labeled samples, as seen in the remote sensing (RS) imagery. In this direction, the present article proposes a semisupervised classification model with spatial information based self-learning methodology to classify land covers in RS images. The model uses granular neural network (GNN) as the base classifier because of its customizable network architecture that is functionally interpretable and costs less computational complexity. Architecture of GNN is governed by fuzzy if–then rules that are generated from fuzzy granulation of input feature space. We have used an improved spatial neighborhood learning method for better understanding of data distribution in a semisupervised framework. The method collects the information with collaborative opinions of two independent information extraction approaches, i.e., based on mutual neighborhood criteria and class map of unlabeled samples. Superiority of the proposed model with existing methods are established with different RS images in terms of various performance measurement indexes.
Highlights
A DVANCEMENT in the sensor technology has increased the information content of remote sensing (RS) images, which can be useful to many applications, e.g., land resource management, soil erosion, and biodiversity
The proposed model used the rule-based granular neural network (GNN) as the base classifier, which had taken the support through the extracted neighborhood information of the unlabeled samples for SSSL
The quality of neighborhood information was improved by using the collaborative opinion of two independent neighborhood information extraction methods
Summary
A DVANCEMENT in the sensor technology has increased the information content of remote sensing (RS) images, which can be useful to many applications, e.g., land resource management, soil erosion, and biodiversity. Various types of sensors are used to collect RS images that result in the generation of diverse data and eventually poses several challenges for classification task. Collection of labeled samples is time consuming and expensive, unlabeled samples are abundantly available. For this reason, semisupervised learning (SSL) based classification models are successfully being used in the RS domain that explore and learn from both limited number of labeled and large number of unlabeled samples. In order to address the issue of limited labeled samples in RS data and the best features for classification task, many research works are being carried out using deep learning architectures [1], [2] and tensor-based methods [3]. The state-of-the-art deep learning models for RS are regarded as special cases of SSL with various deep networks and tuning tricks
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