Abstract
Since the technology of remote sensing has been improved recently, the spatial resolution of satellite images is getting finer. This enables us to precisely analyze the small complex objects in a scene through remote sensing images. Thus, the need to develop new, efficient algorithms like spatial-spectral classification methods is growing. One of the most successful approaches is based on extinction profile (EP), which can extract contextual information from remote sensing data. Moreover, deep learning classifiers have drawn attention in the remote sensing community in the past few years. Recent progress has shown the effectiveness of deep learning at solving different problems, particularly segmentation tasks. This paper proposes a novel approach based on a new concept, which is differential extinction profile (DEP). DEP makes it possible to have an input feature vector with both spectral and spatial information. The input vector is then fed into a proposed straightforward deep-learning-based classifier to produce a thematic map. The approach is carried out on two different urban datasets from Pleiades and World-View 2 satellites. In order to prove the capabilities of the suggested approach, we compare the final results to the results of other classification strategies with different input vectors and various types of common classifiers, such as support vector machine (SVM) and random forests (RF). It can be concluded that the proposed approach is significantly improved in terms of three kinds of criteria, which are overall accuracy, Kappa coefficient, and total disagreement.
Highlights
With the increased spatial resolution of recently produced imaging sensors, a considerable amount of remote sensing satellite images, especially very high-resolution (VHR)images, are available
The classification accuracies are evaluated through four measures, namely overall accuracy (OA), kappa coefficient (K), f-score (F), and total disagreement (T). the first three ones were applied widely in remote sensing applications
This paper proposes a novel approach for the spatial-spectral classification of very high-resolution remote sensing data
Summary
With the increased spatial resolution of recently produced imaging sensors, a considerable amount of remote sensing satellite images, especially very high-resolution (VHR). The spatial information can determine the shape and size of the objects in the image, which is very helpful to reduce the noisy appearance of classified pixels in the final result. Image objects in a scene, especially in urban areas, generally show multiscale or multilevel features They appear at different scales of analysis. Random forest-based methods are fast and yield stable results, their performance is influenced by the size of training samples [42] Another classification method that has been used more recently in the remote sensing community is artificial neural networks. The paper applies a morphological spectrum, including differential extinction profile (DEF) and spectral information, to address the pixel specifications for further classification.
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