【土壤分类】基于matlab GUI多类SVM土壤分类【含Matlab源码 1398期】

一、SVM简介

支持向量机(Support Vector Machine)是Cortes和Vapnik于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。
1 数学部分
1.1 二维空间









2 算法部分

二、部分源代码

% Project Title: Soil Detection & Classification


function varargout = SoilDetect_GUI(varargin)
% SOILDETECT_GUI MATLAB code for SoilDetect_GUI.fig
%      SOILDETECT_GUI, by itself, creates a new SOILDETECT_GUI or raises the existing
%      singleton*.
%
%      H = SOILDETECT_GUI returns the handle to a new SOILDETECT_GUI or the handle to
%      the existing singleton*.
%
%      SOILDETECT_GUI('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in SOILDETECT_GUI.M with the given input arguments.
%
%      SOILDETECT_GUI('Property','Value',...) creates a new SOILDETECT_GUI or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before SoilDetect_GUI_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to SoilDetect_GUI_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help SoilDetect_GUI

% Last Modified by GUIDE v2.5 28-Aug-2021 14:31:16

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @SoilDetect_GUI_OpeningFcn, ...
                   'gui_OutputFcn',  @SoilDetect_GUI_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before SoilDetect_GUI is made visible.
function SoilDetect_GUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to SoilDetect_GUI (see VARARGIN)

% Choose default command line output for SoilDetect_GUI
handles.output = hObject;
handles.output = hObject;
ss = ones(300,400);
axes(handles.axes1);
imshow(ss);
axes(handles.axes2);
imshow(ss);
% Update handles structure
guidata(hObject, handles);

% UIWAIT makes SoilDetect_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = SoilDetect_GUI_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
clc
[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Soil Image');
I = imread([pathname,filename]);
I = imresize(I,[256,256]);
I2 = imresize(I,[300,400]);
axes(handles.axes1);
imshow(I2);title('Query Image');
ss = ones(300,400);
axes(handles.axes2);
imshow(ss);
handles.ImgData1 = I;
guidata(hObject,handles);


% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
I3 = handles.ImgData1;
I4 = imadjust(I3,stretchlim(I3));
I5 = imresize(I4,[300,400]);
axes(handles.axes2);
imshow(I5);title(' Contrast Enhanced ');
handles.ImgData2 = I4;
%% Feature Extraction Part
   % queryimage = I4;
    %queryImage = imresize(queryImage, [256 256]);
    hsvHist = hsvHistogram(I4);
    autoCorrelogram = colorAutoCorrelogram(I4);
    color_moments = colorMoments(I4);
    % for gabor filters we need gary scale image
    img = double(rgb2gray(I4))/255;
    [meanAmplitude, msEnergy] = gaborWavelet(img, 4, 6); % 4 = number of scales, 6 = number of orientations
    wavelet_moments = waveletTransform(I4);
    % construct the queryImage feature vector
    Feature_Vector = [hsvHist autoCorrelogram color_moments meanAmplitude msEnergy wavelet_moments];
    whos Feature_Vector
    F1 = mean2(hsvHist(:));
    F2 = mean2(autoCorrelogram(:));
    F3 = mean2(color_moments(:));
    F4 = mean2(meanAmplitude(:));
    F5 = mean2(msEnergy(:));
    F6 = mean2(wavelet_moments(:));
    
    set(handles.edit3,'string',F1);
    set(handles.edit4,'string',F2);
    set(handles.edit5,'string',F3);
    set(handles.edit6,'string',F4);
    set(handles.edit7,'string',F5);
    set(handles.edit8,'string',F6);
    
    handles.ImgData3 = Feature_Vector;
guidata(hObject,handles);
%%
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load('TrainFeat_Soil.mat')

test = handles.ImgData3;
result = multisvm(TrainFeat,Train_Label,test);
disp(result);

if result == 1
    A1 = 'Clay';
    set(handles.edit1,'string',A1);
    helpdlg(' Clay ');
    disp(' Clay ');
elseif result == 2
    A2 = 'Clayey Peat';
    set(handles.edit1,'string',A2);
    helpdlg(' Clayey Peat ');
    disp('Clayey Peat');
elseif result == 3
    A3 = 'Clayey Sand';
    set(handles.edit1,'string',A3);
    helpdlg(' Clayey Sand ');
    disp(' Clayey Sand ');
elseif result == 4
    A4 = 'Humus Clay';
    set(handles.edit1,'string',A4);
    helpdlg(' Humus Clay ');
    disp(' Humus Clay ');
elseif result == 5
    A5 = 'Peat';
    set(handles.edit1,'string',A5);
    helpdlg(' Peat ');
    disp(' Peat ');
elseif result == 6
    A6 = 'Sandy Clay';
    set(handles.edit1,'string',A6);
    helpdlg(' Sandy Clay ');
    disp('Sandy Clay');
elseif result == 7
    A7 = 'Silty Sand';
    set(handles.edit1,'string',A7);
    helpdlg(' Silty Sand ');
    disp(' Silty Sand ');
end
guidata(hObject,handles);
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton4 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load('Accuracy_Data.mat')
Accuracy_Percent= zeros(200,1);
itr = 500;
hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 500 iterations');
for i = 1:itr
data = Train_Feat;
%groups = ismember(Train_Label,1);
groups = ismember(Train_Label,0);
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = svmclassify(svmStruct,data(test,:),'showplot',false);
classperf(cp,classes,test);
Accuracy = cp.CorrectRate;
Accuracy_Percent(i) = Accuracy.*100;
sprintf('Accuracy of Linear Kernel is: %g%%',Accuracy_Percent(i))
waitbar(i/itr);
end

  

 

  
1
  
2
  
3
  
4
  
5
  
6
  
7
  
8
  
9
  
10
  
11
  
12
  
13
  
14
  
15
  
16
  
17
  
18
  
19
  
20
  
21
  
22
  
23
  
24
  
25
  
26
  
27
  
28
  
29
  
30
  
31
  
32
  
33
  
34
  
35
  
36
  
37
  
38
  
39
  
40
  
41
  
42
  
43
  
44
  
45
  
46
  
47
  
48
  
49
  
50
  
51
  
52
  
53
  
54
  
55
  
56
  
57
  
58
  
59
  
60
  
61
  
62
  
63
  
64
  
65
  
66
  
67
  
68
  
69
  
70
  
71
  
72
  
73
  
74
  
75
  
76
  
77
  
78
  
79
  
80
  
81
  
82
  
83
  
84
  
85
  
86
  
87
  
88
  
89
  
90
  
91
  
92
  
93
  
94
  
95
  
96
  
97
  
98
  
99
  
100
  
101
  
102
  
103
  
104
  
105
  
106
  
107
  
108
  
109
  
110
  
111
  
112
  
113
  
114
  
115
  
116
  
117
  
118
  
119
  
120
  
121
  
122
  
123
  
124
  
125
  
126
  
127
  
128
  
129
  
130
  
131
  
132
  
133
  
134
  
135
  
136
  
137
  
138
  
139
  
140
  
141
  
142
  
143
  
144
  
145
  
146
  
147
  
148
  
149
  
150
  
151
  
152
  
153
  
154
  
155
  
156
  
157
  
158
  
159
  
160
  
161
  
162
  
163
  
164
  
165
  
166
  
167
  
168
  
169
  
170
  
171
  
172
  
173
  
174
  
175
  
176
  
177
  
178
  
179
  
180
  
181
  
182
  
183
  
184
  
185
  
186
  
187
  
188
  
189
  
190
  
191
  
192
  
193
  
194
  
195
  
196
  
197
  
198
  
199
  
200
  
201
  
202
  
203
  
204
  
205
  
206
  
207
  
208
  
209
  
210
  
211
  
212
  
213
  
214
 

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/120800517