搜索资源列表
BP network.matlab平台的基于bp神经网络的遥感图像分类
- matlab平台的基于bp神经网络的遥感图像分类,matlab platform bp neural network-based remote sensing image classification
KMeans-with-opencv k均值分类在遥感影像中的应用
- k均值分类在遥感影像中的应用，完整的c++代码，注释详细易懂，并且已经验证过-k-means classification in remote sensing images in the application, complete c++ code, comments, detailed and easy to understand, and have been verified
ImageRegistration.rar
- 基于边缘特征的图像配准算法源码 基于边缘特征的图像配准算法是将不同时间、不同的传感器（成像设备）或不同条件下（天候、照度、摄像位置和角度等）获取的同一场景的两幅或多幅图像根据其边缘特征进行匹配、叠加的处理，最终生成一幅全景图像的方法。该方法具有抗噪性强，匹配速度快，误匹配率低，适用于多种类型的图像等优点，主要可以运用于以下领域： （1）军事研究领域，如飞行器辅助导航系绞、武器投射系统的末制导以及寻地等应用研究； （2）医学图像分析，如数字剪影血管造影DSA血管造影术、肿瘤检测、白内障检测、
fuzzy_c_means.rar
- 本程序用c编写，主要用于对遥感图像进行聚类（非监督分类）。,This programme is used to for clustering images (unsupervised classifciation)
confusionmatrix
- 混淆矩阵的每一列代表了地面参考验证信息，每一列中的数值等于地表真实像元在分类图象中对应于相应类别的数量；混淆矩阵的每一行代表了遥感数据的分类信息，每一行中的数值等于遥感分类像元在地表真实像元相应类别中的数量。 -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
shiyan1
- 遥感图像分类描述，有代码，也有图像，用Matlab实现-Remote sensing image classification descr iption, code, there are images, achieved with the Matlab
Matlab_Classification_based_on_BP
- 基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征，并训练BP网络，再利用网络进行分类-BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP networ
Image_Classify
- 遥感影像分类的matlab实现（源码+图像）。以及分类后评价（总体精度、Kappa系数、混淆矩阵）。-Remote Sensing Image Classification of matlab implementation (source code+ images). After the evaluation and classification (overall accuracy, Kappa coefficient, confusion matrix).
Image
- 遥感图像的打开处理程序，能够打开单波段，多波段图像，几何图像变换，线性拉伸变换，平滑 处理包括并行和串行，锐化处理包括梯度锐化、Roberts锐化、laplace锐化、sobel锐化等，还 有用绝对距离和马氏距离算法进行的监督分类算法等，包括了RAW格式数据资源-The opening of remote sensing image processing, to open the single-band, multi-band images, geometric image
K_means
- 此软件的功能是对遥感图像进行非监督分类。实现算法是k均值-This software is a remote sensing image for non-supervised classification. K-means algorithm implementation are
Bayes_Classify
- 基于贝叶斯最小错误率算法的崇明岛遥感图像分类程序。并且有饼状图显示各类地物比例-Minimum error rate based on Bayesian algorithm for Chongming Island, the remote sensing image classification procedures. And the pie chart below shows the proportion of various types of features
Wavelet_Classify
- 基于小波变换和K-Mean算法的崇明东滩遥感图像分类，读取样本的能量特征向量进行分类，并返回分类图像的RGB图，显示各类地物面积比例-Based on wavelet transform and the K-Mean Algorithm for remote sensing image classification Chongming Dongtan, the energy to read a sample feature vectors for classification, and retu
fcm_color
- 模糊c均值聚类算法对遥感图像分类，可自己进行修改-Fuzzy c means clustering algorithm for remote sensing image classification can be modify their
xiangsidu
- 遥感图像的多光谱目标识别，相似度方法实现分类-The multi-spectral remote sensing image object recognition, similarity classification method
bpandkohonen
- 神经网络源码，可应用于遥感图像的分类，采用的包括bp、kohonen。可以作为范例来学习。-Neural network source code can be used in remote sensing image classification, using the included bp, kohonen. Can serve as examples to learn.
fenlei1
- 利用提出的纹理灰度值进行最小距离分类遥感图像-Using the proposed minimum distance of gray value texture classification of remote sensing image
knn_classify
- knn分类算法 遥感图象分类 knn分类算法 遥感图象分类 -knn classify k-nearest neighbor algorithm In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space
Classification-OfTheRemoteImage
- 基于纹理及光谱信息融合的遥感图像分类方法研究，硕士论文，有关纹理和遥感图像分类很好的学习素材-Texture and spectral information based on the integration of remote sensing image classification method, master' s thesis, the classification of remote sensing image texture and good learning material
遥感数字图像处理
- 较为系统地讲述了遥感数字图像处理必备的基础知识，遥感图像预处理、增强处理、遥感图像的监督分类和非监督分类等基本理论。(The basic knowledge of remote sensing digital image processing, the basic theory of remote sensing image preprocessing, enhancement processing, supervised classification and unsupervised clas
random forest
- 利用matlab编程来实现多光谱遥感数据的随机森林分类(Random Forest Classification by Multispectral Remote Sensing)