Deep Learning Spatial-Spectral Classification of Remote Sensing Images by Applying Morphology-Based Differential Extinction Profile (DEP)
| Autor: | Nafiseh Kakhani, Mehdi Mokhtarzade, Mohammad Javad Valadan Zoej |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
TK7800-8360
extinction profile (EP) deep learning segmentation spatial-spectral classification remote sensing image Computer Networks and Communications Computer science Feature vector Classifier (linguistics) Segmentation Electrical and Electronic Engineering Image resolution Spatial analysis Remote sensing business.industry Deep learning Random forest Support vector machine Hardware and Architecture Control and Systems Engineering Signal Processing Artificial intelligence Electronics business |
| Zdroj: | Electronics; Volume 10; Issue 23; Pages: 2893 Electronics, Vol 10, Iss 2893, p 2893 (2021) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10232893 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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