Classification maps for the WHLK dataset using 0.2% training samples.

Classification maps for the WHLK dataset using 0.2% training samples.

<p>(a) False-color image. (b) Ground-truth (GT). (c–h) The classification maps with disparate algorithms.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yuping Yin (3617510) (author)
مؤلفون آخرون:	Haodong Zhu (14412015) (author), Lin Wei (70005) (author)
منشور في:	2025
الموضوعات:	Cell Biology Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified robust feature representation hyperspectral images based experimental results demonstrate convolutional neural networks layer feature fusion hyperspectral image classification proposed network backbone channel reconstruction convolutions multiple scconv modules channel reconstruction scconv modules convolution layer scconv ), classification tasks classification effectiveness usually impacted thereby obtaining testing time storage capacity spectral features significant advantages scnet ). paper proposes model complexity effectively utilize different depths computational power complex computations cnn ).
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
الملخص:	<p>(a) False-color image. (b) Ground-truth (GT). (c–h) The classification maps with disparate algorithms.</p>

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