Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.

Abstract The Changning–Menglian orogenic belt (CMOB) in the southeastern Tibetan Plateau is an important link between the Longmu Co–Shuanghu suture (LCSS) in the northern Tibetan Plateau and the Chiang Mai–Inthanon and Bentong–Raub sutures in Thailand and Peninsular Malaysia. These belts and sutures are generally regarded as containing the remnants of the oceanic crust of the Palaeo-Tethys that formed by seafloor spreading as a result of the separation of Gondwana- and Eurasia-derived blocks during the Middle Cambrian. In this paper we report the first discovery of abundant unaltered and retrograde eclogites that occur as irregular lenses and blocks in metasedimentary rocks of the CMOB, and these eclogites form an elongate and almost N–S-trending high pressure (HP)–ultrahigh pressure (UHP) metamorphic belt that is ∼200 km long and ∼50 km wide. The newly discovered phengite/talc/epidote–glaucophane eclogites, lawsonite–talc–phengite eclogites, dolomite/magnesite–kyanite eclogites and phengite–kyanite-bearing retrograde eclogites have E- and N-MORB-like affinities and mainly positive as well as some negative whole-rock εNd values (–4.34 to + 7.89), which suggest an enriched and depleted oceanic lithosphere source for their protoliths. Magmatic zircons separated from the epidote–glaucophane, magnesite–kyanite and (phengite–kyanite-bearing) retrograde eclogites gave protolith ages of 317–250 Ma, which fit well within the time frame of the opening of the Palaeo-Tethys during the Middle Cambrian and its closure during the Triassic. Abundant metamorphic zircons in the eclogites indicate a Triassic metamorphic event related to the subduction of the Palaeo-Tethys oceanic crust from 235 to 227 Ma. Taking into account previous isotopic age data, we now establish the periods of Early–Middle Triassic (246–227 Ma) and Late Triassic (222–209 Ma) as the ages of subduction and exhumation of the Palaeo-Tethyan oceanic crust, respectively. Thermodynamic modelling revealed that the eclogites record distinct HP–UHP peak metamorphic conditions of 23.0–25.5 kbar and 582–610 °C for the phengite–glaucophane eclogites, 24.0–25.5 kbar and 570–586 °C for the talc–glaucophane eclogites, 29.0–31.0 kbar and 675–712 °C for the dolomite–kyanite eclogites, and 30.0–32.0 kbar and 717–754 °C for the magnesite–kyanite eclogites. These P–T estimates and geochronological data indicate that the Palaeo-Tethys oceanic slab was subducted to different mantle depths from 75 km down to 95 km, forming distinct types of eclogite with a variety of peak eclogite-facies mineral assemblages. The eclogites consistently record clockwise metamorphic P–T–t paths characterised by a heating–compression prograde loop under a low geothermal gradient of 5–10 °C/km, indicating the rapid subduction of cold oceanic crust at a rate of 4.5–6.0 km/myr, followed by isothermal or cooling–decompressive retrogression and exhumation at an average rate of 3.2–4.2 km/myr. The newly discovered eclogites of the CMOB with their signatures of ocean-crust subduction are petrologically, geochemically and geochronologically comparable to those of the LCSS, providing powerful support for the idea that a nearly 2000-km-long HP–UHP eclogite belt extends from the northern Tibetan Plateau to the southeastern Tibetan Plateau, and that it represents the main boundary suture of the Palaeo-Tethyan domain. These results have far-reaching implications for the tectonic framework and complex metamorphic evolution of the Palaeo-Tethyan domain.

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