Cross-scene hyperspectral image classification combined spatial-spectral domain adaptation with XGBoost
نویسندگان
چکیده
é对跨åºæ¯é«å è°±é¥æå¾ååç±»ä¸æºååç®æ åçé¢è°±å移é®é¢ï¼æåºä¸ç§ç»å空谱åéåºä¸æ度梯度æåæ ï¼eXtreme Gradient Boostingï¼ XGBoostï¼çè·¨åºæ¯é«å è°±å¾åå类模åãå°æ·±åº¦è¶ åæ°å·ç§¯æ¨¡åï¼Depthwise Over-parameterized Convolution Modelï¼DOCMï¼åå¤§æ ¸æ³¨æåï¼Large Kernel Attentionï¼LKAï¼ç»åï¼ææ空谱注æå模åï¼æåæºå空谱ç¹å¾ãå©ç¨ç¸åç空谱注æå模å对ç®æ åè¿è¡ç¹å¾æåï¼å¹¶ä¸é´å«å¨å®æ对æåéåºï¼åå°æºåä¸ç®æ åä¹é´çé¢è°±å移ï¼éè¿ç®æ åä¸å°éææ ç¾æ°æ®å¯¹ç®æ åç¹å¾æåå¨è¿è¡æçç£åéåºï¼ä½¿ç®æ åç¹å¾æåå¨è¿ä¸æ¥å¦ä¹ ç®æ åççå®åå¸ï¼å¹¶å¯¹æºååç®æ åçç¹å¾è¿è¡æ å°ï¼å½¢æç¸ä¼¼ç空é´åå¸ï¼å®æèç±»åéåºãæåï¼ä½¿ç¨éæåç±»å¨XGBoostè¿è¡é«å è°±å¾ååç±»ï¼è¿ä¸æ¥æé«æ¨¡åçè®ç»é度ä¸ç½®ä¿¡åº¦ãå¨PaviaåIndianaé«å è°±æ°æ®éä¸çå®éªç»æ表æï¼æ¬æç®æ³çæ»ä½å类精度åå«è¾¾å°äº91.62%å 65.98%ãç¸æ¯è¾äºå ¶ä»è·¨åºæ¯é«å è°±å¾åå类模åï¼æ¬æææ模åå ·ææ´é«çå°ç©å类精度ã
منابع مشابه
Sample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملSpectral-Spatial Response for Hyperspectral Image Classification
This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboring regions. SSR forms a deep architecture and is able to learn discriminative spectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based ...
متن کاملCross-domain CNN for Hyperspectral Image Classification
In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral dat...
متن کاملSpectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectralspatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyper-spectral image is classified using a pixel-wise classif...
متن کاملSpectral/Spatial Hyperspectral Image Compression
^Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County, Baltimore, MD 21250 ^Computer Science Department, University of Extremadura Avda. de la Universidad s/n,10.071 Caceres, SPAIN ^Center for Space and Remote Sensing Research Graduate Institute of Space Science Department of Computer Science and...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Guangxue jingmi gongcheng
سال: 2023
ISSN: ['1004-924X']
DOI: https://doi.org/10.37188/ope.20233113.1950