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تسجيل مستخدم جديدLand cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
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In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an objects orientation and on a sensors flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotat
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